Search

Universities are currently strengthening the field of researchfrom academia. However, the spectacular results are overshadowed by the amount of information. One strategy is to work to present the achievements of institutional research in an easy and understandable way. Supported by the information offered by Colciencias called “science innumbers”, the institutions propose to generate a pilot with some ofthe main reports, which transcend to graphic presentations, and whichare easily compatible with other institutional instances. The result of thiswork is Ephimerus, a semi-automatic system to implement queries and generate reports in a graphical, easy and friendly way, so that they can be absorbed in the daily work of the administrative team that, within the institution, is in charge of research., Actualmente las Universidades vienen fortaleciendo el campo de la Investigación desde la academia. Sin embargo, la espectacularidad de los resultados se opaca con la cantidad de información. Una estrategia, radica en trabajar para presentar de una manera fácil y más comprensible, los logros de la investigación institucional. Apoyados en la información ofertada por MinCiencias denominada “la ciencia en cifras”, las instituciones proponen generar un piloto, con algunos de los principales reportes, que trasciendan a presentaciones gráficas, y que resulten fácilmente compatibles con otras instancias institucionales. Resultado de esta labor, surge Ephimerus, un sistema semiautomático, para implementarlas consultas y generar los reportes de manera gráfica, fácil y amigable, para que puedan ser absorbidas en el quehacer diario del equipo administrativo que, dentro de la institución, tienen a cargo la investigación.

As data interoperability has become increasingly more important as of late, so has the topic of database query language translations. Being able to translate between different query languages, both old and new, could potentially improve a lot of different systems. One of these systems is HeFQUIN, a federated query engine that primarily focuses on querying many different RDF data sources by sending out SPARQL sub-queries. Exploring ways to extend this engine to include non-RDF data sources, like GraphQL for example, would naturally increase the uses of such an engine. This thesis presents, and implements, a generalized approach for translating SPARQL queries, primarily Basic Graph Patterns (BGPs), into GraphQL queries, after which the resulting JSON response received by executing the query is translated back to solution mappings. The performance of the translator implementation is then evaluated by using a set of pre-determined, specially designed, test queries. These queries are structured to encompass many different scenarios the proposed translator could face. The results of the evaluation tests reveal that the translator implementation with its steps only constitute a comparatively small fraction of the full request to the GraphQL endpoints. Finally, it is concluded that due in part to the generalized nature of the approach, a wide range of different structured basic graph patterns are able to be translated, as long as they follow the set up requirements imposed on the BGP and the GraphQL endpoint.

ODIN és un sistema que exposa una ontologia, que és la conceptualització de les fonts de dades i el domini d'interès, amb l'objectiu d'oferir una interfície de consultes uniformes. Les consultes sobre l'ontologia es reescriuen sobre les fonts a través de mapejos d'esquema. La part del sistema que involucra la construcció automàtica de l'ontologia a partir de les fonts de dades i els seus respectius mapejos es coneix com a bootstrapping. L'objectiu d'aquest TFG és millorar el bootstrapping actual del sistema ODIN i implementar tots els casos d'ús i funcionalitats que falten per tal que funcioni per a qualsevol font de dades JSON., ODIN is a system that exposes an ontology, which is the conceptualization of the source data and the domain of interest, with the aim of offering a uniform query interface. Queries over the ontology are rewritten over the sources via schema mappings. The part of the system that involves the automatic construction of the ontology from the data sources and their respective mappings is known as bootstrapping. The objective of this TFG is to improve the current bootstrapping of the ODIN system and to implement all the use cases and functionalities that are missing.

This paper contributes to the discussion initiated in a recent SIGIR paper describing a gap in the information retrieval (IR) literature on query understanding–where they come from and whether they serve their purpose. Particularly the connection between query variability and search engines regarding consistent and equitable access to all users. We focus on a user group typically underserved: children. Using preliminary experiments (based on logs collected in the classroom context) and arguments grounded in children IR literature, we emphasize the importance of dedicating research efforts to interpreting queries formulated by children and the information needs they elicit. We also outline open problems and possible research directions to advance knowledge in this area, not just for children but also for other often-overlooked user groups and contexts., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Web Information Systems

As data interoperability has become increasingly more important as of late, so has the topic of database query language translations. Being able to translate between different query languages, both old and new, could potentially improve a lot of different systems. One of these systems is HeFQUIN, a federated query engine that primarily focuses on querying many different RDF data sources by sending out SPARQL sub-queries. Exploring ways to extend this engine to include non-RDF data sources, like GraphQL for example, would naturally increase the uses of such an engine. This thesis presents, and implements, a generalized approach for translating SPARQL queries, primarily Basic Graph Patterns (BGPs), into GraphQL queries, after which the resulting JSON response received by executing the query is translated back to solution mappings. The performance of the translator implementation is then evaluated by using a set of pre-determined, specially designed, test queries. These queries are structured to encompass many different scenarios the proposed translator could face. The results of the evaluation tests reveal that the translator implementation with its steps only constitute a comparatively small fraction of the full request to the GraphQL endpoints. Finally, it is concluded that due in part to the generalized nature of the approach, a wide range of different structured basic graph patterns are able to be translated, as long as they follow the set up requirements imposed on the BGP and the GraphQL endpoint.

[ES] En este trabajo se ha desarrollado un análisis sobre un listado de expresiones de búsqueda relacionas con industrias culturales en un motor de búsqueda. El objetivo principal de este estudio es encontrar una metodología de trabajo efectiva sobre conjuntos masivos de datos consistentes en expresiones de búsqueda o palabras clave extraídas de motores de búsqueda, con independencia de su temática. Para llevarlo a cabo se partió de un listado de expresiones de búsqueda relacionas con industrias culturales extraídas de Google mediante el uso de la herramienta Google Keyword Planner. Tras probar diferentes conjuntos de datos y herramientas especializadas en análisis de grafos, finalmente se decidió trabajar con una muestra aleatoria del 5% de los datos originales y el programa Gephi. A partir de esta muestra se creó una matriz que enfrentaba cada búsqueda de la muestra con el resto de búsquedas y recogía el número de palabras que coincidían en cada caso. Además, se eliminó la diagonal de la matriz y los conectores más comunes de las búsquedas para evitar sesgos y ruido. Con esta matriz y mediante el algoritmo Fruchterman Reingold se obtuvo un grafo formado por 1.506 nodos y 28.242 aristas que contenía 27 comunidades, siendo la comunidad más grande y céntrica, la correspondiente al conjunto formado por las expresiones contenedoras de la palabra clave “libros”. Dados los resultados, les puede considerar que la metodología final propuesta es efectiva y cabría tenerla en cuenta para poder replicarla en el futuro a una escala mayor, [EN] In this paper an analysis has been developed on a list of queries related to cultural industries in a search engine. The main objective of the study is to find an effective working methodology on massive datasets consisting of queries or keywords extracted from search engines, regardless of their subject matter. To carry it out, we started from a list of search expressions related to cultural industries extracted from Google using the Google Keyword Planner tool. After testing different data sets and specialized tools in graph analysis, it was decided to work with a random sample of a 5% of the original dataset and the Gephi software. From this sample, a matrix was created that compared each query of the sample with the rest of the queries and collected the number of words that matched in each case. In addition, the diagonal of the matrix and the most common connectors of the searches were eliminated to avoid bias and noise. With this matrix and working with the Fruchterman Reingold algorithm, a graph formed by 1,506 nodes and 28,242 edges was obtained that contained 27 communities, where the largest and most central community being the one corresponding to the set formed by the expressions containing the keyword “libros”. Given the results, it can be considered that the final proposed methodology is effective and should be taken into account to be able to replicate it in the future on a larger scale

[ES] En este trabajo se ha desarrollado un análisis sobre un listado de expresiones de búsqueda relacionas con industrias culturales en un motor de búsqueda. El objetivo principal de este estudio es encontrar una metodología de trabajo efectiva sobre conjuntos masivos de datos consistentes en expresiones de búsqueda o palabras clave extraídas de motores de búsqueda, con independencia de su temática. Para llevarlo a cabo se partió de un listado de expresiones de búsqueda relacionas con industrias culturales extraídas de Google mediante el uso de la herramienta Google Keyword Planner. Tras probar diferentes conjuntos de datos y herramientas especializadas en análisis de grafos, finalmente se decidió trabajar con una muestra aleatoria del 5% de los datos originales y el programa Gephi. A partir de esta muestra se creó una matriz que enfrentaba cada búsqueda de la muestra con el resto de búsquedas y recogía el número de palabras que coincidían en cada caso. Además, se eliminó la diagonal de la matriz y los conectores más comunes de las búsquedas para evitar sesgos y ruido. Con esta matriz y mediante el algoritmo Fruchterman Reingold se obtuvo un grafo formado por 1.506 nodos y 28.242 aristas que contenía 27 comunidades, siendo la comunidad más grande y céntrica, la correspondiente al conjunto formado por las expresiones contenedoras de la palabra clave “libros”. Dados los resultados, les puede considerar que la metodología final propuesta es efectiva y cabría tenerla en cuenta para poder replicarla en el futuro a una escala mayor, [EN] In this paper an analysis has been developed on a list of queries related to cultural industries in a search engine. The main objective of the study is to find an effective working methodology on massive datasets consisting of queries or keywords extracted from search engines, regardless of their subject matter. To carry it out, we started from a list of search expressions related to cultural industries extracted from Google using the Google Keyword Planner tool. After testing different data sets and specialized tools in graph analysis, it was decided to work with a random sample of a 5% of the original dataset and the Gephi software. From this sample, a matrix was created that compared each query of the sample with the rest of the queries and collected the number of words that matched in each case. In addition, the diagonal of the matrix and the most common connectors of the searches were eliminated to avoid bias and noise. With this matrix and working with the Fruchterman Reingold algorithm, a graph formed by 1,506 nodes and 28,242 edges was obtained that contained 27 communities, where the largest and most central community being the one corresponding to the set formed by the expressions containing the keyword “libros”. Given the results, it can be considered that the final proposed methodology is effective and should be taken into account to be able to replicate it in the future on a larger scale

[ES] En este trabajo se ha desarrollado un análisis sobre un listado de expresiones de búsqueda relacionas con industrias culturales en un motor de búsqueda. El objetivo principal de este estudio es encontrar una metodología de trabajo efectiva sobre conjuntos masivos de datos consistentes en expresiones de búsqueda o palabras clave extraídas de motores de búsqueda, con independencia de su temática. Para llevarlo a cabo se partió de un listado de expresiones de búsqueda relacionas con industrias culturales extraídas de Google mediante el uso de la herramienta Google Keyword Planner. Tras probar diferentes conjuntos de datos y herramientas especializadas en análisis de grafos, finalmente se decidió trabajar con una muestra aleatoria del 5% de los datos originales y el programa Gephi. A partir de esta muestra se creó una matriz que enfrentaba cada búsqueda de la muestra con el resto de búsquedas y recogía el número de palabras que coincidían en cada caso. Además, se eliminó la diagonal de la matriz y los conectores más comunes de las búsquedas para evitar sesgos y ruido. Con esta matriz y mediante el algoritmo Fruchterman Reingold se obtuvo un grafo formado por 1.506 nodos y 28.242 aristas que contenía 27 comunidades, siendo la comunidad más grande y céntrica, la correspondiente al conjunto formado por las expresiones contenedoras de la palabra clave “libros”. Dados los resultados, les puede considerar que la metodología final propuesta es efectiva y cabría tenerla en cuenta para poder replicarla en el futuro a una escala mayor, [EN] In this paper an analysis has been developed on a list of queries related to cultural industries in a search engine. The main objective of the study is to find an effective working methodology on massive datasets consisting of queries or keywords extracted from search engines, regardless of their subject matter. To carry it out, we started from a list of search expressions related to cultural industries extracted from Google using the Google Keyword Planner tool. After testing different data sets and specialized tools in graph analysis, it was decided to work with a random sample of a 5% of the original dataset and the Gephi software. From this sample, a matrix was created that compared each query of the sample with the rest of the queries and collected the number of words that matched in each case. In addition, the diagonal of the matrix and the most common connectors of the searches were eliminated to avoid bias and noise. With this matrix and working with the Fruchterman Reingold algorithm, a graph formed by 1,506 nodes and 28,242 edges was obtained that contained 27 communities, where the largest and most central community being the one corresponding to the set formed by the expressions containing the keyword “libros”. Given the results, it can be considered that the final proposed methodology is effective and should be taken into account to be able to replicate it in the future on a larger scale

A framework for private and secure communication and interaction between agents interacting in transportation services is developed. An agent, i.e. a user, can ask questions or submit queries regarding whether the other agents, i.e. drivers, use the desired road at specific times of the day in an encrypted fashion. The authors developed the framework using semi-homomorphic encryption (namely, the Paillier's encryption method) to enable the algebraic manipulation of plain data without the need for decryption using appropriate computations over the encrypted data. Strong privacy and security guarantees are proved for the agents. Subsequently, the semi-homomorphic encryption method is utilised to develop privacy-aware ride-sharing and routing algorithms without the need for disclosing the origin and destination of the user., QC 20210329

This project constructs a different cost-based pushdown police solution for querying in multidimensional environments. The integration of Qbeast, a novel index, in the Cassandra distributed database caused the need from frameworks, as Spark, to be aware and act in consecuence. We will see three approaches, the last one of them in a theorical frame: filter pushdown, sampling and a speculative physic data strategy. Each one of their implementations are detailed in the document, alongside with an explanation of the class modified. The solutions were tested with mixed data volumns, to see in which cases is efficient to follow that path. Results show that with low rows the new behaviour goes hand in hand with the default, but in intensive cases (starting with one gigabyte files) the speed-up begins to grow.

We study the minimum weight basis problem on matroid when elements' weights are uncertain. For each element we only know a set of possible values (an uncertainty area) that contains its real weight. In some cases there exist bases that are uniformly optimal, that is, they are minimum weight bases for every possible weight function obeying the uncertainty areas. In other cases, computing such a basis is not possible unless we perform some queries for the exact value of some elements. Our main result is a polynomial time algorithm for the following problem. Given a matroid with uncertainty areas and a query cost function on its elements, find the set of elements of minimum total cost that we need to simultaneously query such that, no matter their revelation, the resulting instance admits a uniformly optimal base. We also provide combinatorial characterizations of all uniformly optimal bases, when one exists; and of all sets of queries that can be performed so that after revealing the corresponding weights the resulting instance admits a uniformly optimal base.

El problema de les cerques per rangs multidimensionals és un problema clàssic per al qual des de fa unes quantes dècades s'havien anat proposant i utilitzant multitud d'estructures de dades multidimensionals de diversa índole. No obstant això, treballs experimentals recents indiquen que gràcies a les característiques del hardware modern, un simple escanejat de totes les dades resulta més eficient, per a una àmplia gamma de cerques, que la utilització de les estructures de dades clàssiques. Però per a nosaltres, era difícil resignar-se a no poder trobar una solució millor que l'escanejat lineal. A més, la nostra intuïció ens indicava que havia d'haver alguna manera d'utilitzar tot el coneixement previ sobre estructures de dades multidimensionals per trobar una solució més eficient a aquest problema. Així doncs, en aquest treball proposem un nou mètode per resoldre el problema de les cerques per rang multidimensionals. La nostra proposta combina la utilització d'estructures de dades multidimensionals clàssiques com a part del preprocés per a crear bounding boxes, amb la utilització d'un dynamic pipeline per tractar les dades preprocessades i generar el resultat. Experimentalment, mostrem que el nostre mètode és, efectivament, més eficient que l'escanejat lineal de les dades en una àmplia gamma de conjunts de dades i diferents configuracions de rangs multidimensionals en fer les consultes., The Multidimensional Range Query problem is a classic one for which for the last few decades a multitude of Multidimensional Data Structures of various kinds had been proposed and used. However, recent experimental work indicates that thanks to the characteristics of modern hardware, a simple scan of all data is more efficient for a wide range of searches than the use of data structures. But for us, it was difficult to resign ourselves to not being able to find a better solution than scanning. In addition, our intuition told us that there must be some way to use all previous knowledge about Multidimensional Data Structures to find a more efficient solution to this problem. Thus, in this paper, we propose a new method to solve the problem of Multidimensional Range Queries. Our proposal combines the use of classic Multidimensional Data Structures as part of the preprocess to create bounding boxes, with the use of a dynamic pipeline to process the preprocessed data and generate the result. Experimentally, we show that our method is, effectively, more efficient than the linear scanning of the data in a wide range of data sets and different multidimensional range configurations when making queries.

Querying graph databases has recently received much attention. We propose a new approach to this problem, which balances competing goals of expressive power, language clarity and computational complexity. A distinctive feature of our approach is the ability to express properties of minimal (e.g. shortest) and maximal (e.g. most valuable) paths satisfying given criteria. To express complex properties in a modular way, we introduce labelling-generating ontologies. The resulting formalism is computationally attractive - queries can be answered in non-deterministic logarithmic space in the size of the database.

We present the first polynomial time algorithm to learn nontrivial classes of languages of infinite trees. Specifically, our algorithm uses membership and equivalence queries to learn classes of omega-tree languages derived from weak regular omega-word languages in polynomial time. The method is a general polynomial time reduction of learning a class of derived omega-tree languages to learning the underlying class of omega-word languages, for any class of omega-word languages recognized by a deterministic Büchi acceptor. Our reduction, combined with the polynomial time learning algorithm of Maler and Pnueli [Maler and Pneuli, Inform. Comput., 1995] for the class of weak regular omega-word languages yields the main result. We also show that subset queries that return counterexamples can be implemented in polynomial time using subset queries that return no counterexamples for deterministic or non-deterministic finite word acceptors, and deterministic or non-deterministic Büchi omega-word acceptors. A previous claim of an algorithm to learn regular omega-trees due to Jayasrirani, Begam and Thomas [Jayasrirani et al., ICGI, 2008] is unfortunately incorrect, as shown in [Angluin, YALEU/DCS/TR-1528, 2016].

Based on corpora and earlier studies, this paper mainly attempts to answer the question how constructions of restrictive [i]which[/i] and [i]that[/i] have developed in comparison to one another in American and British English news until today. Corpus queries are designed to match patterns of particular object and subject gap constructions, such as [i]I like the ball that/which is green[/i] with subject gap and [i]It is the toy that/which I prefer[/i], which has object gap. Each query generates a [i]query set[/i], which includes all search hits for that query. Rather than checking all entries in every query set, entries of randomized samples are verified manually. From each such sample, the proportion of relevant entries, [i]relevance index[/i], is calculated. Relevance index helps us to estimate the relevant frequencies of the query sets. These estimations are essential for calculation of [i]frequency indexes[/i], which compare how frequencies of [i]that[/i] and [i]which[/i] clauses have progressed over time. In British English, the results are mixed with opposite tendencies for different time periods and news categories. As for American English, all data consistently support a significant frequency increase in [i]that[/i] with a corresponding decline in restrictive [i]which[/i].

BACKGROUND: Tick paralysis, resultant from envenomation by the scrub-tick Ixodes holocyclus, is a serious threat for small companion animals in the eastern coast of Australia. We hypothesise that surveillance systems that are built on Internet search queries may provide a more timely indication of high-risk periods more effectively than current approaches. METHODS: Monthly tick paralysis notifications in dogs and cats across Australia and the states of Queensland (QLD) and New South Wales (NSW) were retrieved from Disease WatchDog surveillance system for the period 2011-2013. Internet search terms related to tick paralysis in small companion animals were identified using Google Correlate, and corresponding search frequency metrics were downloaded from Google Trends. Spearman's rank correlations and time series cross correlations were performed to assess which Google search terms lead or are synchronous with tick paralysis notifications. RESULTS: Metrics data were available for 24 relevant search terms at national level, 16 for QLD and 18 for NSW, and they were all significantly correlated with tick paralysis notifications (P < 0.05). Among those terms, 70.8, 56.3 and 50 % showed strong Spearman's correlations, at national level, for QLD, and for NSW respectively, and cross correlation analyses identified searches which lead notifications at national or state levels. CONCLUSION: This study demonstrates that Internet search metrics can be used to monitor the occurrence of tick paralysis in companion animals, which would facilitate early detection of high-risk periods for tick paralysis cases. This study constitutes the first application of the rapidly emerging field of Internet-based surveillance to veterinary science.

The study focuses on building multibase data storages that consider a correlation between the data properties and performed queries. This type of data storaging has been neither viewed as an approach nor researched before. Lack of attention especially concerns presentation of data by various models for optimizing query response.We suggest a method of designing multibase data storages on the basis of data structuredness, which allows posting the reference data in storage media the data models of which facilitate performing queries on them. The efficiency of the designed data storage is optimized on the basis of the statistics on queries processing and consists in storing data as well as saving the data in storage media with the help of indexing, materialized submission, fragmentation, and merger. We have studied both the impact of design phases and optimization on storage performance and the parameters of the modified genetic algorithm, including the threshold of gene adaptation.The research has proved that application of the suggested approach increases the integral index of query processing by 10 %. The storage building time can be reduced to 50 %, which significantly impacts data storage building of a huge amount of data. An important advantage of the approach is flexibility: any storage media and optimization mechanisms can be used while applying the suggested models.

In this paper we use information retrieval metrics to evaluate the effect of a document sanitization process, measuring information loss and risk of disclosure. In order to sanitize the documents we have developed a semi-automatic anonymiza-tion process following the guidelines of Executive Order 13526 (2009) of the US Administration. It embodies two main and independent steps: (i) identifying and anonymizing specific person names and data, and (ii) concept generalization based on WordNet categories, in order to identify words categorized as classified. Finally, we manually revise the text from a contextual point of view to eliminate complete sentences, paragraphs and sections, where necessary. For empirical tests, we use a subset of the Wikileaks Cables, made up of documents relating to five key news items which were revealed by the cables. © Springer International Publishing Switzerland 2015

This paper examines the ways of different types inquiries to Wolfram|Alpha, in particular, the possibility of natural language queries. The characteristic of mobile access to Wolfram|Alpha are highlighted. The data representation format was considered to visual representations, images, HTML, Mathematica Cell, text view, plain text, MathML, Mathematica input, Mathematica output, audio presentation, special types of Wolfram|Alpha output.The paper aim is the analysis and synthesis of capabilities for requests to Wolfram|Alpha in many ways and opportunities to provide characteristics of mobile access to Wolfram|Alpha.The subject of the study is Wolfram|Alpha as cloud-based service of mathematics learning.In preparing the paper material used case studies and experimental research presentation capabilities of different types of requests to Wolfram|Alpha.Conclusion: Wolfram|Alpha work is based on natural language processing (currently only English), a large library of algorithms and NKS-approach to answering queries. Wolfram|Alpha does not issue a list of links based on query results, and calculates the answer based on their own knowledge base. Service is able to convert data between different measurement units, counting systems, etc.

This paper examines the ways of different types inquiries to Wolfram|Alpha, in particular, the possibility of natural language queries. The characteristic of mobile access to Wolfram|Alpha are highlighted. The data representation format was considered to visual representations, images, HTML, Mathematica Cell, text view, plain text, MathML, Mathematica input, Mathematica output, audio presentation, special types of Wolfram|Alpha output.The paper aim is the analysis and synthesis of capabilities for requests to Wolfram|Alpha in many ways and opportunities to provide characteristics of mobile access to Wolfram|Alpha.The subject of the study is Wolfram|Alpha as cloud-based service of mathematics learning.In preparing the paper material used case studies and experimental research presentation capabilities of different types of requests to Wolfram|Alpha.Conclusion: Wolfram|Alpha work is based on natural language processing (currently only English), a large library of algorithms and NKS-approach to answering queries. Wolfram|Alpha does not issue a list of links based on query results, and calculates the answer based on their own knowledge base. Service is able to convert data between different measurement units, counting systems, etc.

Information Retrieval (IR) systems aim to retrieve data that satisfies certain requirements and constitute an important service in many types of networks, including Delay/Disruption Tolerant Networks (DTNs). In current DTN based IR systems, the data that satisfies a query is assumed to be stored on a single node. Therefore, once a node receives a query in which it has the corresponding data, the query can be resolved completely. However, in scenarios where a query requires data from multiple nodes, these IR systems may fail. Henceforth, in this paper, we propose Distributed Data-Centric Information Retrieval (DDC-IR), a data centric IR system that supports all query types; e.g., continuous and complex. More importantly, it is designed specifically to operate in DTNs. It also incorporates a new packet, aka Query Reply Packet, that includes both a query and one or more replies. We show how this packet facilitates efficient query resolution and enables data centric routing. In addition, it uses caching so that nodes store popular queries that has the effect of speeding up query resolution. We have conducted an extensive simulation study to compare DDC-IR to state of the art IR systems using the popular Random Waypoint model and a trace-file containing student movements on a campus. The results show that DDC-IR is able to resolve 50 % more queries and has an 80 % lower buffer occupancy level than existing IR systems. We also tested DDC-IR in networks with varying sizes. For networks with 100 nodes, DDC-IR is able to resolve queries while current IR systems fail to resolve any queries. In particular, when the number of nodes increases, current IR systems fail to resolve any queries, whilst DDC-IR is able to resolve complex and continuous queries. The influence of the number of sub-queries on query resolution time is also studied. Specifically, when the number of sub-queries in a complex query increases from five to nine, DDC-IR uses 50 % more time to resolve a query. In

This paper examines the ways of different types inquiries to Wolfram|Alpha, in particular, the possibility of natural language queries. The characteristic of mobile access to Wolfram|Alpha are highlighted. The data representation format was considered to visual representations, images, HTML, Mathematica Cell, text view, plain text, MathML, Mathematica input, Mathematica output, audio presentation, special types of Wolfram|Alpha output.The paper aim is the analysis and synthesis of capabilities for requests to Wolfram|Alpha in many ways and opportunities to provide characteristics of mobile access to Wolfram|Alpha.The subject of the study is Wolfram|Alpha as cloud-based service of mathematics learning.In preparing the paper material used case studies and experimental research presentation capabilities of different types of requests to Wolfram|Alpha.Conclusion: Wolfram|Alpha work is based on natural language processing (currently only English), a large library of algorithms and NKS-approach to answering queries. Wolfram|Alpha does not issue a list of links based on query results, and calculates the answer based on their own knowledge base. Service is able to convert data between different measurement units, counting systems, etc.

Many efforts have been done in the last years to model public transport timetables in order to find optimal routes. The proposed models can be classified into two types: those representing the timetable as an array, and those representing it as a graph. The array-based models have been shown to be very effective in terms of query time, while the graph-based models usually answer queries by computing shortest paths, and hence they are suitable to be used in combination with speed-up techniques developed for road networks. In this paper, we focus on the dynamic behavior of graph-based models considering the case where transportation systems are subject to delays with respect to the given timetable. We make three contributions: (i) we give a simplified and optimized update routine for the well-known time-expanded model along with an engineered query algorithm; (ii) we propose a new graph-based model tailored for handling dynamic updates; (iii) we assess the effectiveness of the proposed models and algorithms by an experimental study, which shows that both models require negligible update time and a query time which is comparable to that required by some array-based models.

We live in an era of information overload. From online news to online shopping to scholarly research, we are inundated with a torrent of information on a daily basis. With our limited time, money and attention, we often struggle to extract actionable knowledge from this deluge of data. A common approach for addressing this challenge is personalization where results are automatically filtered to match the tastes and preferences of individual users. While showing promise, modern systems and algorithms for personalization face their own set of challenges, both technical and social in nature. On the technical side, these include the well-documented cold start problem, redundant result sets and an inability to move beyond simple user interactions, such as keyword queries and star ratings. From a social standpoint studies have shown that most Americans have negative opinions of personalization, primarily due to privacy concerns. In this thesis, we address these challenges by introducing interactive concept coverage, a general framework for personalization that incentivizes diversity, and applies in both queryless settings as well as settings requiring complex and rich user interactions. This framework involves framing personalized recommendation as a probabilistic budgeted max-cover problem where each item to be recommended is defined to probabilistically cover one or more concepts. From user interaction, we learn weights on concepts and affinities for items, such that solving the resulting optimization problem results in personalized, diverse recommendations. Theoretical properties of our framework guarantee efficient, near-optimal solutions to our objective function, and no-regret learning of user preferences., Supported in part by N00014-08-1-0752, N00014-10-1-0672, NSF-IIS0644225, NSF-CNS-064422518, NSF-CNS-0721591 and ARO W911NF-07-1-0287 and W911NF-08-1-0242.

The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimization of queries with single kNN predicates, and shows how queries with two kNN predicates can be optimized. In particular, the paper addresses the optimization of queries with: (i) two kNN-select predicates, (ii) two kNN-join predicates, and (iii) one kNN-join predicate and one kNN-select predicate. For each type of queries, conceptually correct query evaluation plans (QEPs) and new algorithms that optimize the query execution time are presented. Experimental results demonstrate that the proposed algorithms outperform the conceptually correct QEPs by orders of magnitude.

Indexing is a crucial technique for dealing with the massive amount of data present on the web. In our third participation in the web track at TREC 2012, we explore the idea of building an efficient query-based indexing system over Web page collection. Our prototype explores the trends in user queries and consequently indexes texts using particular attributes available in the documents. This paper provides an in-depth description of our approach for indexing web documents efficiently; that is, topics available in the web documents are discovered with the assistance of knowledge available in Wikipedia. The well-defined articles in Wikipedia are shown to be valuable as a training set when indexing Webpages. Our complex index structure also records information from titles and urls, and pays attention to web domains. Our approach is designed to close the gaps in our approaches from the previous two years, for some queries. Our framework is able to efficiently index the 50 million pages available in the subset B of the ClueWeb09 collection. Our preliminary experiments on the TREC 2012 testing queries showed that our indexing scheme is robust and efficient for both indexing and retrieving relevant web pages, for both the ad-hoc and diversity task., Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

In this paper, we introduce the PITT group's methods and findings in TREC 2012 session track. After analyzing the search logs in session track 2011 and 2012 datasets, we find that users' reformulated queries are very different from their previous ones, probably indicating their expectations to find not only relevant but also novel results. However, as indicated from our results, a major approach adopted by the session track participants, i.e. using relevance feedback information extracted from previous queries for search, will sacrifice the novelty of results for improving ad hoc search performance (e.g. nDCG@10). Such issues were not disclosed in previous years' session tracks because TREC did not consider the effects of duplicate results in evaluation. Therefore, we proposed a method to properly penalize the duplicate results in ranking by simulating users' browsing behaviors in a search session. A duplicate result in current search will be penalized to a greater extent if it was ranked in higher positions in previous searches or it was returned by more previous queries. The method can effectively improve the novelty of search results and lead to only slight and insignificant drop in ad hoc search performance., Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

The goal of the TREC 2012 Medical Records Track was to search medical record documents to identify patients as possible candidates for clinical studies based on diagnosis, age, and other attributes. For TREC 2012, the Oregon Health & Science University (OHSU) group experimented with both manual and automated techniques. We used a derivative of Lucene to build an interactive retrieval system that can process queries in one of two ways. Users can manually specify Boolean queries whose terms may include words as well as ICD-9 codes. Alternatively, the system features an automated query parser that transforms free-text queries into structured Boolean queries. The query parser is built on top of MetaMap and the UMLS Metathesaurus. We submitted both automatic runs (which relied solely on the automated query parser) as well as manual runs consisting of queries built by an expert clinician. Overall, our automated query parser performed below the mean of other groups, although there were individual topics for which it performed very well. This irregular performance was in part due to our parser's tendency to over-specify queries, leading to reduced recall. There were, however, several topics for which our parser performed very well, suggesting that our fundamental approach has merit. In contrast, our manual runs performed very well, scoring second-best among official manual runs. With further modification of the manual queries, we were able to achieve even better performance. Query of electronic health records for the use case of identifying patients as candidates for clinical studies still requires manual query development, at least until better automated methods can be developed that outperform them., Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

In this paper, we report the experiments we conducted for our participation to the TREC 2012 Web Track. We experimented a brand new system that models the latent concepts underlying a query. We use Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, to exhibit highly-specific query-related topics from pseudo-relevant feedback documents. We define these topics as the latent concepts of the user query. Our approach automatically estimates the number of latent concepts as well as the needed amount of feedback documents, without any prior training step. These concepts are incorporated into the ranking function with the aim of promoting documents that refer to many different query-related thematics. We also explored the use of different types of sources of information for modeling the latent concepts. For this purpose, we use four general sources of information of various nature (web, news, encyclopedic) from which the feedback documents are extracted., Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

Electronic Medical Records (EMRs) have greatly expanded the potential for the evidence-based improvement of clinical practice by providing a data source for computable medical information. The Text REtrieval Conference 2012 Medical Records Track (TREC-med) explored how information retrieval may support clinical research by providing an efficient means to identify cohorts for clinical studies. A shared task called participants to find cohorts of relevant patients for 50 different topic queries. The users in TREC-med information retrieval systems would be medical experts who are searching for cohorts. In our previous work, we have collaborated with such experts on specific queries; the assortment of 50 queries makes this competition a standardized benchmark task. Thus, techniques that have shown case-by-case improvement can be tested against a much larger number of queries. We have taken this opportunity to investigate three core questions around which many of our algorithms are designed: 1. What is the relative value of structured data (e.g., fields in EMRs, or document metadata) compared to clinical text? 2. Are extensive information extraction (IE) efforts any benefit when we consider the applied question of information retrieval (IR)? 3. Can distributional semantics help supply missing information in a query?, Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

This paper describes our work (the IIEIR participation) in the TREC 2012 Microblog Adhoc Track. We proposed a ranking algorithm with temporal information based query. More and more research work proved that time is an important factor for improving the search result, especially for Microblog search. Based on Language Model, the representative work used time information as the document's prior information. Intuitively, there were two ways for making use of this feature. One way was query relevant while the other was query irrelevant. The hypothesis of the two models is the newer of the document, the more important. However, different query had different hot time points (the top time of relevance documents' time distribution). Take this into consideration; we supposed four models based on hot time points (HTLM). On this basis, we considered the model which is not relevant with query as document's background information and the model which is relevant with query as document's independent information. We used smoothing operation and supposed a mix timed language model. The results suggested that HTLM models are more effective for Microblog search and mix model further improved compared with the single model., Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

In this work, we emphasize on formulating effective structured queries for session search. For a given query, phrase-like text nuggets are identified and formulated into Lemur queries to feed into the Lemur search engine. Nuggets are substrings in qn, similar to phrases but not necessarily as semantically coherent as phrases. We assume that a valid nugget appears frequently in top returned snippets for qn. In this work the longest sequences of words consisting of frequent bigrams within the top returned snippets are identified as nuggets and are used to formulate a new query. By formulating structured query using the nuggets, we greatly boost the search accuracy than just using qn. We experiment both strict and relaxed forms of structured query formulation. The strict form of query formulation achieves an improvement of 13.5% and the relaxed form achieves an improvement of 17.8% on nDCG at 10 on TREC 2011 query sets. We further combine the nuggets generated from all queries q1, ..., qn-1, qn, to formulate one structured session query for the entire session. Nuggets from each query are weighed by various weighting schemes to indicate their relations to the current query and their potential contributions to the retrieval performance. We experiment three weighting schemes, uniform (all queries share the same weight), previous vs. current (previous queries q1, ..., qn-1 share the same weight while qn uses a different and higher weight), and distance-based (the weights are distributed based on how far a query;s position in the session is from the current query). We find that previous vs. current achieves the best search accuracy. For retrieval, we first retrieve a large pool of documents for qn. We then employ a re-ranking model that considers document similarity between clicked documents and documents in the pool as well as dwell time., Presented at the Twenty-First Text REtrieval Conference (TREC 2012) held in Gaithersburg, Maryland, November 6-9, 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). U.S. Government or Federal Rights License

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g., iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100 faster than Apache Hive, and machine learning programs up to 100 faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engines provide. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.

We have considered the differences between questions and requests, and their co-presence in the structure of queries. Because of the so-called “descriptive fallacy” in philosophy of language, it took rather a long time to give them the attention they were due. Thanks to pragmatics, this oversight has been rectified. Asking questions testifies to the strong relationship between lack of determinacy (poverty, both in knowledge and in action) and the need to overcome it (in order to attain plentifulness). Interrogative structures are devices where triggers such as wh-words or suspended assent are at work to retrieve missing information, extract knowledge, or receive the cooperation requested. Answers are therefore not only assertions, but also permissions, prohibitions, orders, suggestions, etc. The logico-linguistic structure which is always required across this variety of speech acts and which makes possible the wording of questions and requests is predication. Even in elliptical or simply verbless sentences, predication is at work albeit implicit or implied. To be at work means that it is a necessary condition for the complete efficiency and comprehensibility of the sentence itself. To be at work, then, means that the addressee/hearer/reader has to bear in mind, or retrieve, the predication, where the absence of recognition would prevent him/her from understanding the meaning, i.e. the semantics of the sentence. In crosstalk such as “Ready?” “Not yet.”, no verb appears, but predication is easily recognisable, as implicit (in the question) or implied (in the answer): Implicit, specifically as a part of the first turn “[Are you] ready?”, and implied as the whole turn upon which negation operates. The role of negation is in fact that of an operator, the scope of which is the whole preceding sentence structure: [It is] not yet [true that I am ready]”, i.e. the preceding sentence deprived of its interrogative mood, that is to say without the suspension of assent typical o

The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimization of queries with single kNN predicates, and shows how queries with two kNN predicates can be optimized. In particular, the paper addresses the optimization of queries with: (i) two kNN-select predicates, (ii) two kNN-join predicates, and (iii) one kNN-join predicate and one kNN-select predicate. For each type of queries, conceptually correct query evaluation plans (QEPs) and new algorithms that optimize the query execution time are presented. Experimental results demonstrate that the proposed algorithms outperform the conceptually correct QEPs by orders of magnitude.

Federated search (federated information retrieval or distributed information retrieval) is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot easily index uncrawlable hidden web collections while federated search systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated search systems need to acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. The goal of this work is to provide a comprehensive summary of the previous research on the federated search challenges described above.

The emerging popularity of multimedia data, as digital representation of text image, video and countless other milieus, with prodigious volumes and wild diversity exhibits the phenomenal impact of modern technologies in reforming the way information is accessed, disseminated, digested and retained. This has iteratively ignited the data-driven perspective of research and development, to characterize perspicuous patterns, crystallize informative insights, and realize elevated experience for end-users, where innovations in a spectrum of areas of computer science, including databases, distributed systems, machine learning, vision, speech and natural languages, has been incessantly absorbed and integrated to elicit the extent and efficacy of contemporary and future multimedia applications and solutions. Under the theme of pattern mining and similarity querying, this manuscript presents a number of pieces of research concerning multimedia data, to address an array of practical tasks encompassing automatic annotation, outlier detection community discovery, multi-modal retrieval and learning to rank, in their respective contexts including satellite image analysis, internet traffic surveillance, image bioinformatics andWeb search. A repertoire of extant and novel techniques pertaining to graph mining, clustering analysis, tensor decomposition and probabilistic graphical models has been developed or adapted, which satisfactorilymet differing quality and efficiency requisites postulated by specific application settings, best exemplified by the 40 times speed-up in annotating satellite images and the up to 30% performance improvement in predicting web search user clicks, yet without the loss of generality to similar and related scenarios.

In this paper, we consider authentication codes where the adversary has access to a verification oracle. We formally study two attack games: offline attack and online attack. In an offline impersonation attack with verification query of order i, the adversary launches its attack through two stages. In the first stage - the query stage - the adversary can adaptively choose i distinct messages to query the verification oracle. The verification oracle will answer whether these queried messages are valid or invalid under the secret encoding rule agreed by the transmitter and the receiver. In the later stage - the spoofing stage - the adversary creates a fraudulent message which is different from all its queried messages and sends this message to the receiver. The adversary wins if the receiver accepts the fraudulent message as a valid message. In an online impersonation attack with verification query of order i, the adversary has i + 1 chances to query the verification oracle and wins as soon as one of the queries is a valid message. We make use of strategy trees, which allow optimal strategies in both attack games to be identified, to establish a number of relationships between the value of the two games. This allows us to formally prove a relationship between the value of the game when the adversary has i queries, and the one in which he does not have any. The relationship, though widely believed to be true, was only recently proved for computationally secure systems. Our result complements this latter work for the information theoretic setting.

In this paper, we consider authentication codes where the adversary has access to a verification oracle. We formally study two attack games: offline attack and online attack. In an offline impersonation attack with verification query of order i, the adversary launches its attack through two stages. In the first stage - the query stage - the adversary can adaptively choose i distinct messages to query the verification oracle. The verification oracle will answer whether these queried messages are valid or invalid under the secret encoding rule agreed by the transmitter and the receiver. In the later stage - the spoofing stage - the adversary creates a fraudulent message which is different from all its queried messages and sends this message to the receiver. The adversary wins if the receiver accepts the fraudulent message as a valid message. In an online impersonation attack with verification query of order i, the adversary has i + 1 chances to query the verification oracle and wins as soon as one of the queries is a valid message. We make use of strategy trees, which allow optimal strategies in both attack games to be identified, to establish a number of relationships between the value of the two games. This allows us to formally prove a relationship between the value of the game when the adversary has i queries, and the one in which he does not have any. The relationship, though widely believed to be true, was only recently proved for computationally secure systems. Our result complements this latter work for the information theoretic setting.

In this paper, we consider authentication codes where the adversary has access to a verification oracle. We formally study two attack games: offline attack and online attack. In an offline impersonation attack with verification query of order i, the adversary launches its attack through two stages. In the first stage - the query stage - the adversary can adaptively choose i distinct messages to query the verification oracle. The verification oracle will answer whether these queried messages are valid or invalid under the secret encoding rule agreed by the transmitter and the receiver. In the later stage - the spoofing stage - the adversary creates a fraudulent message which is different from all its queried messages and sends this message to the receiver. The adversary wins if the receiver accepts the fraudulent message as a valid message. In an online impersonation attack with verification query of order i, the adversary has i + 1 chances to query the verification oracle and wins as soon as one of the queries is a valid message. We make use of strategy trees, which allow optimal strategies in both attack games to be identified, to establish a number of relationships between the value of the two games. This allows us to formally prove a relationship between the value of the game when the adversary has i queries, and the one in which he does not have any. The relationship, though widely believed to be true, was only recently proved for computationally secure systems. Our result complements this latter work for the information theoretic setting.

In this paper, we consider authentication codes where the adversary has access to a verification oracle. We formally study two attack games: offline attack and online attack. In an offline impersonation attack with verification query of order i, the adversary launches its attack through two stages. In the first stage - the query stage - the adversary can adaptively choose i distinct messages to query the verification oracle. The verification oracle will answer whether these queried messages are valid or invalid under the secret encoding rule agreed by the transmitter and the receiver. In the later stage - the spoofing stage - the adversary creates a fraudulent message which is different from all its queried messages and sends this message to the receiver. The adversary wins if the receiver accepts the fraudulent message as a valid message. In an online impersonation attack with verification query of order i, the adversary has i + 1 chances to query the verification oracle and wins as soon as one of the queries is a valid message. We make use of strategy trees, which allow optimal strategies in both attack games to be identified, to establish a number of relationships between the value of the two games. This allows us to formally prove a relationship between the value of the game when the adversary has i queries, and the one in which he does not have any. The relationship, though widely believed to be true, was only recently proved for computationally secure systems. Our result complements this latter work for the information theoretic setting.

Consider a bin containing n balls colored with two colors. In a k-query, k balls are selected by a questioner and the oracle's reply is related (depending on the computation model being considered) to the distribution of colors of the balls in this k-tuple; however, the oracle never reveals the colors of the individual balls. Following a number of queries the questioner is said to determine the majority color if it can output a ball of the majority color if it exists, and can prove that there is no majority if it does not exist. We investigate two computation models (depending on the type of replies being allowed). We give algorithms to compute the minimum number of 3-queries which are needed so that the questioner can determine the majority color and provide tight and almost tight upper and lower bounds on the number of queries needed in each case.

Federated search (federated information retrieval or distributed information retrieval) is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot easily index uncrawlable hidden web collections while federated search systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated search systems need to acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. The goal of this work is to provide a comprehensive summary of the previous research on the federated search challenges described above.

This paper describes the PKUTM participation in the TREC 2010 Blog Track. We only concentrated on the Faceted Blog Distillation Task this year. Our system adopts a two-stage approach for this task. In the first stage, our system makes use of an IR platform - indri to obtain the top N ad-hoc topic-relevant blog posts for each query. In the second stage, different models are designed to identify the facet inclination. The experimental results show the effectiveness of our approach., Presented at the Text REtrieval Conference (TREC 2010) (19th) held in Gaithersburg, Maryland on 16-19 November 2010. Published in the Proceedings of the Text REtrieval Conference (TREC 2010) (19th), 2010. Sponsored in part by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA).

In this paper we describe Carnegie Mellon University's sub-mission to the TREC 2010 Web Track. Our baseline run combines different methods, of which in particular the spam prior and mixture model were found the most effective. We also experimented with expansion over the Wikipedia corpus and found that picking the right Wikipedia articles for expansion can improve performance substantially. Furthermore, we did preliminary experiments with combining expansion over the Wikipedia corpus with expansion over the top ranked web pages., Presented at the Text REtrieval Conference (TREC 2010) (19th) held in Gaithersburg, Maryland on 16-19 November 2010. Published in the Proceedings of the Text REtrieval Conference (TREC 2010) (19th), 2010. Sponsored in part by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA).

This paper gives an overview of our work for related entity finding which is proposed in TREC 2010 Entity Track. The goal of the Entity Track is to find the entities relevant to a given query from the web corpus. In this paper, we propose a bipartite graph reinforcement model for entity ranking. As is well known, the entities on the web are embedded not only in the natural language text, but also in the tables and lists. Given a query, both the candidate entities and relevant tables/lists are extracted from web documents. Then the candidate entities extracted from unstructured text are ranked based on a probabilistic model. But the result contains a lot of noise. If some candidate entities are in a relevant table/list, they are more relevant to the given query. And Vice versa, if a table/list contains several candidate entities, it is also more relevant to the query. Based on the above intuition, we construct a bipartite graph and then perform a reinforcement algorithm to re-rank the candidate entities., Presented at the Text REtrieval Conference (TREC 2010) (19) held in Gaithersburg, Maryland on 16-19 November 2010. Published in the Proceedings of the Text REtrieval Conference (TREC 2010) (19th), 2010. Sponsored in part by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA).

In this paper we provide an overview of the Webis group's two-phase approach to the TREC 2010 Sessions track. In a preprocessing phase the queries are segmented to highlight contained concepts. In the final retrieval phase we treat Carnegie Mellon's ClueWeb search engine as a black box and apply the MAXIMUM QUERY framework., Presented at the Text REtrieval Conference (TREC 2010) (19th) held in Gaithersburg, Maryland on 16-19 November 2010. Published in the Proceedings of the Text REtrieval Conference (TREC 2010) (19th), 2010. Sponsored in part by the National Institute of Standards and Technology (NIST), the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA).

The use of Private Information Retrieval (PIR) techniques enable clients to retrieve items from cooperating databases without revealing either the queries or the information being retrieved. In order to prevent clients from accessing information that they are not authorized to access, it must be possible to prove that the queries being posed are compliant with a set of privacy policies previously agreed upon by the clients and database owners. Efforts to address privacy in these situations have been dominated by techniques that assume that most clients are malicious and focus on helping database owners restrict access to data. With the current push towards need-to-share, we suggest alternative approaches such as the application of accountability mechanisms. These mechanisms include the use of formalisms that can express realistic data-use policies, automated reasoning engines that can interpret those policies, automatically determining whether particular queries are policy-compliant, and justifications to enable users to understand the compliance decision and the policies.

Migració i reprogramació de la base de dades de Affinity i continguts veterinaris, maquetació i programació del nou front-office a una nova pàgina dinàmica similar a la actual, però amb millores significatives en quant a disseny i consultes més clares que facin més fàcil la navegació a l’usuari. Aquest sistema havia de ser accessible per mitjà d’Internet per a diferents tipus d’usuaris(veterinaris,estudiants,auxiliarsveterinaris,particulars...). Els administradors en aquest cas serien tant el client com l’empresa en un principi, per poder fer modificacions i actualitzar components si se escau. Vistes les necessitats del client es pretén solucionar el problema, realitzant un portal web que cobrirà aquests objectius, sent una aplicació de fàcil accés i ús per a tots els usuaris, amb opcions intuïtives, fàcils d’entendre i amb un mínim de manteniment, on s’ha de mostrar la informació d’una manera eficientment estructurada, de forma que la seva consulta sigui ràpida i efectiva. I donat que el nombre de publicacions del client és elevat ha sigut convenient implantar un motor de recerca intern per a facilitar el seu accés. La nova interfície web estarà estructurada en diferents fitxers, basats essencialment en llenguatge ASP, el qual es un llenguatge potent alhora de realitzar pàgines dinàmiques i fer creacions de queries contra la base de dades.

NPS's Annual Acquisition Research Symposium (6th), Monterey CA, 13-14 May 2009. The original document contains color images.