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This paper addresses the problem of sharing person-specific genomic sequences without violating the privacy of their data subjects to support large-scale biomedical research projects. The proposed method builds on the framework proposed by Kantarcioglu et al. [1] but extends the results in a number of ways. One improvement is that our scheme is deterministic, with zero probability of a wrong answer (as opposed to a low probability). We also provide a new operating point in the space-time tradeoff, by offering a scheme that is twice as fast as theirs but uses twice the storage space. This point is motivated by the fact that storage is cheaper than computation in current cloud computing pricing plans. Moreover, our encoding of the data makes it possible for us to handle a richer set of queries than exact matching between the query and each sequence of the database, including: (i) counting the number of matches between the query symbols and a sequence; (ii) logical OR matches where a query symbol is allowed to match a subset of the alphabet thereby making it possible to handle (as a special case) a “not equal to” requirement for a query symbol (e.g., “not a G”); (iii) support for the extended alphabet of nucleotide base codes that encompasses ambiguities in DNA sequences (this happens on the DNA sequence side instead of the query side); (iv) queries that specify the number of occurrences of each kind of symbol in the specified sequence positions (e.g., two ‘A’ and four ‘C’ and one ‘G’ and three ‘T’, occurring in any order in the query-specified sequence positions); (v) a threshold query whose answer is ‘yes’ if the number of matches exceeds a query-specified threshold (e.g., “7 or more matches out of the 15 query-specified positions”). (vi) For all query types, we can hide the answers from the decrypting server, so that only the client learns the answer. (vii) In all cases, the client deterministically learns only the query's answer, except for query type (v) where we quantify the (very small) statistical leakage to the client of the actual count.

Query expansion term selection methods are really very important for improving the accuracy and efficiency of pseudo-relevance feedback based automatic query expansion for information retrieval system by removing irrelevant and redundant terms from the top retrieved feedback documents corpus with respect to user query. Individual query expansion term selection methods have been widely investigated for improving its performance. However, it is always a challenging task to find an individual query expansion term selection method that would outperform other individual query expansion term selection methods in most cases. In this paper, first we explore the possibility of improving the overall performance using individual query expansion term selection methods. Second, we propose a model for combining multiple query expansion term selection methods by using rank combination approach, called multiple ranks combination based query expansion. Third, semantic filtering is used to filter semantically irrelevant term obtained after combining multiple query expansion term selection methods, called ranks combination and semantic filtering based query expansion. Fourth, the genetic algorithm is used to make an optimal combination of query terms and candidate term obtained after rank combination and semantic filtering approach, called semantic genetic filtering and rank combination based query expansion. Our experimental results demonstrated that our proposed approaches achieved significant improvement over each individual query expansion term selection method and related state-of-the-art approaches.

The effectiveness of query expansion methods depends essentially on identifying good candidates, or prospects, semantically related to query terms. Word embeddings have been used recently in an attempt to address this problem. Nevertheless query disambiguation is still necessary as the semantic relatedness of each word in the corpus is modeled, but choosing the right terms for expansion from the standpoint of the un-modeled query semantics remains an open issue. In this paper we propose a novel query expansion method using word embeddings that models the global query semantics from the standpoint of prospect vocabulary terms. The proposed method allows to explore query-vocabulary semantic closeness in such a way that new terms, semantically related to more relevant topics, are elicited and added in function of the query as a whole. The method includes candidates pooling strategies that address disambiguation issues without using exogenous resources. We tested our method with three topic sets over CLEF corpora and compared it across different Information Retrieval models and against another expansion technique using word embeddings as well. Our experiments indicate that our method achieves significant results that outperform the baselines, improving both recall and precision metrics without relevance feedback.

The arrival of large voluminous continuous queries sets for a given query leads an insignificant insights. The elimination of certain data tuples occurs in order to balance the system load. The streaming query removes the improper data tuples and uses proper data tuples in the form of defined tables or sets. However, major drawback occurs due to unbounded streaming and inadequate access to end data. Due to such constraints, many stream processing methods makes the processed data unavailable for any applications or to the related queries of neighborhood branches. This paper avoids such problems during the process of data tuples at the generation of queries. The study uses a streaming model that executes effective query plans in continuous data. The streaming model aims reduce the communication cost and improves the scalability of continuous aggregation queries. It sub-divides the client query and executes it over data aggregators within the incoherent limit. A weighted dominant query algorithm is formulated to provide the top dominant value in each sub-query clusters. This reduces the cost for computation in synthetic databases. The experimental results proved that the proposed model with weighted dominant query algorithm effectively improves scalability by reducing the computational cost.

We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.

Query reverse engineering seeks to re-generate the SQL query that produced a given query output table from a given database. In this paper, we solve this problem for OLAP queries with group-by and aggregation. We develop a novel three-phase algorithm named REGAL 1 for this problem. First, based on a lattice graph structure, we identify a set of group-by candidates for the desired query. Second, we apply a set of aggregation constraints that are derived from the properties of aggregate operators at both the table-level and the group-level to discover candidate combinations of group-by columns and aggregations that are consistent with the given query output table. Finally, we find a multi-dimensional filter, i.e., a conjunction of selection predicates over the base table attributes, that is needed to generate the exact query output table. We conduct an extensive experimental study over the TPC-H dataset to demonstrate the effectiveness and efficiency of our proposal.

The state-of-the-art indexing mechanisms for distributed cloud data management systems can not support complex queries, such as multi-dimensional query and range query. To solve this problem, we propose a multi-dimensional indexing mechanism named PR-Chord to support complex queries. PR-Chord is composed of the global index named PR-Index and the Chord network. The multi-dimensional space formed by the range of the multi-dimensional data is divided into hyper-rectangle spaces equally. The PR-Index is a hierarchical index structure based on the improved PR quadtree to index these spaces. The complex query is transformed into the query of leaf nodes of PR-Index. We design the algorithms of query, insertion and deletion to support complex queries. Since PR-Index does not store the multi-dimensional data, its maintenance cost is zero. PR-Chord has the advantages of load balancing and simple algorithm. The experiment results demonstrate that PR-Chord has good query efficiency.

Efficient processing of skyline queries with partially ordered domains has been intensively addressed in recent years. To further reduce the query processing time to support high-responsive applications, the skyline queries that were previously processed with user preferences similar to those of the new query contribute useful candidate result points. Hence, the answered queries can be cached with both their results and the user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of cached queries with compatible user preferences. When caching a significant number of queries accumulated over time, it is essential to adopt effective access methods to index the cached queries to retrieve a set of relevant cached queries for facilitating the cache-based skyline query computations. In this paper, we propose an extended depth-first search indexing method (e-DFS for short) for accessing user preference profiles represented by directed acyclic graphs (DAGs), and emphasize the design of the e-DFS encoding that effectively encodes a user preference profile into a low-dimensional feature point which is eventually indexed by an R-tree. We obtain one or more traversal orders for each node in a DAG by traversing it through a modified version of the depth-first search which is utilized to examine the topology structure and dominance relations to measure closeness or similarity. As a result, e-DFS which combines the criteria of similarity evaluation is able to greatly reduce the search space by filtering out most of the irrelevant cached queries such that the query processor can avoid accessing the entire data set to compute the query results. Extensive experiments are presented to demonstrate the performance and utility of our indexing method, which outperforms the baseline planning techniques by reducing 37 percent of the computational time on average.

Keyword search over databases, popularized by keyword search in WWW, allows ordinary users to access database information without the knowledge of structured query languages and database schemas. Most of the previous studies in this area use IR-style ranking, which fail to consider the importance of the query answers. In this paper, we propose CI-Rank , a new approach to keyword search in databases, which considers the importance of individual nodes in a query answer and the cohesiveness of the result structure in a balanced way. CI-Rank is built upon a carefully designed model called Random Walk with Message Passing that helps capture the relationships between different nodes in the query answer. This model lends itself well to adaptation to user preferences. We develop a branch and bound algorithm to support the efficient generation of top-k query answers. Indexing methods are also introduced to further speed up the run-time processing of queries. Extensive experiments conducted on two real data sets with a real user query log confirm the effectiveness and efficiency of CI-Rank .

Data integration systems on the Deep Web offer a transparent means to query multiple data sources at once. Result merging– the generation of an overall ranked list of results from different sources in response to a query– is a key component of a data integration system. In this work we present a result merging model, called Active Relevance Weight Estimation model. Different from the existing techniques for result merging, we estimate the relevance of a data source in answering a query at query time. The relevances for a set of data sources are expressed with a (normalized) weighting scheme: the larger the weight for a data source the more relevant the source is in answering a query. We estimate the weights of a data source in each subset of the data sources involved in a training query. Because an online query may not exactly match any training query, we devise methods to obtain a subset of training queries that are related to the online query. We estimate the relevance weights of the online query from the weights of this subset of training queries. Our experiments show that our method outperforms the leading merging algorithms with comparable response time.

Currently, most of the processing techniques for the conventional location-based queries focus only on a single type of objects. However, in real-life applications, the user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. We term the different types of stationary objects closer to each other the heterogeneous neighboring objects ( HNOs ). Efficient processing of the location-based queries on the HNOs is more complicated than that on a single data source, because the neighboring relationship between the HNOs inevitably affects the query result. In this paper, we present useful and important location-based aggregate queries on the HNOs , which can provide useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. The location-based aggregate queries consist of four queries: the shortest average-distance ( SAvgD ) query, the shortest minimal-distance ( SMinD ) query, the shortest maximal-distance ( SMaxD ) query, and the shortest sum-distance ( SSumD ) query. To process the location-based aggregate queries, we devise two heuristics, the HNOs-qualifying heuristic and the HNOs-pruning heuristic , to efficiently determine the HNOs sets. According to different query types, we further propose four heuristics, the SAvgD-pruning heuristic , the SMinD-pruning heuristic , the SMaxD-pruning heuristic , and the SSumD-pruning heuristic , to effectively reduce the number of distance computations required for query processing. Comprehensive experiments are conducted to demonstrate the effectiveness of the heuristics and the efficiency of the proposed approaches.

Dataspaces provide a co-existence approach for heterogeneous data. Relationships among these heterogeneous data are often incrementally identified, such as object associations or attribute synonyms. With the different degree of relationships recognized, various query answers may be obtained. In this paper, we review the major techniques for processing and optimizing queries in dataspaces, according to their different abilities of handling relationships, including 1) simple search query without considering relationships, 2) association query over object associations, 3) heterogeneity query with attribute correspondences, and 4) similarity query for similar objects. Techniques such as indexing, query rewriting, expansion, and semantic query optimization are discussed for these query types. Finally, we highlight possible directions in accessing dataspaces.

Composing queries is evidently a tedious task. This is particularly true of graph queries as they are typically complex and prone to errors, compounded by the fact that graph schemas can be missing or too loose to be helpful for query formulation. Despite the great success of query formulation aids, in particular, automatic query completion , graph query autocompletion has received much less research attention. In this demonstration, we present a novel interactive visual subgraph query autocompletion framework called A uto G which alleviates the potentially painstaking task of graph query formulation. Specifically, given a large collection of small or medium-sized graphs and a visual query fragment q formulated by a user, A uto G returns top- k query suggestions Q ′ as output at interactive time. Users may choose a query from Q ′ and iteratively apply A uto G to compose their queries. We demonstrate various features of A uto G and its superior ability to generate high quality suggestions to aid visual subgraph query formulation.

There are plentiful and diverse applications of graph data management and mining techniques in the real-world scientific research and business activities. As one of the most basic operations, uniform path pattern query processing on graph data faces three big challenges. In this paper, we deal with these challenges by the following points. Firstly, a new query language on graph, called G-Path, is presented, which focuses on complex path pattern query processing on a very large graph. Also, the design of a system called Para-G is proposed, which is based on a BSP-like model as well as MapReduce model, and can effectively handle distributed graph data operations and queries. Secondly, the implementation of Para-G on the de facto cloud platform -- Hadoop -- is brought forward. Based on the concept of distributed path finite state automaton, the query processing of a G-Path statement in Para-G is detailed. In addition, as the query optimization of G-Path queries, several tricks are utilized to dramatically improve the performance of query execution. Finally, extensive experiments on several graph data sets are conducted to show the usability of the G-Path query language and the effectiveness of Para-G.

static query form based on user's requirement is very difficult task. Because these static queries are predefined and shows only constant results so user cannot get satisfactory result. Here, static versus dynamic query forms are compared where static query form is constant and dynamic query form is changed according to the user need. Searching and retrieving database from the heterogeneous data is very critical task. The traditional databases do not satisfy the user desired instances. These databases contain constant fields and may not change according to the user's requirement. To get the user satisfactory result, dynamic database proposed which create dynamic query forms. In dynamic database generating fields are specified by the user which is iterative process, at every iteration system generates ranked list for the fields and then user automatically add these fields to the query form. The ranking of fields is based on user's preference. To view the result user fills out the query forms at each iteration and submit the results. KeywordsQF (Dynamic Query Form), CQF (Customized Query Form), SQF (Static Query Form).

Recently, Reverse k Nearest Neighbors (RkNN) queries, returning every answer for which the query is one of its k nearest neighbors, have been extensively studied on the database research community. But the RkNN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RSTkNN query to find these objects, taking both spatial and textual similarity into consideration. However, the RSTkNN query cannot control the size of answer set and to be sorted according to the degree of influence on the query. In this paper, we propose a new problem Ranked Reverse Boolean Spatial Keyword Nearest Neighbors query called Ranked-RBSKNN query, which considers both spatial similarity and textual relevance, and returns t answers with most degree of influence. We propose a separate index and a hybrid index to process such queries efficiently. Experimental results on different real-world and synthetic datasets show that our approaches achieve better performance.

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty-answer set, our framework dynamically computes and suggests alternative queries with fewer conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty-answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. Moreover, we discuss two important extensions to the base framework: the specification of a minimum size on the number of results returned by a relaxed query and the possibility of proposing multiple conditions at the same time. We analyze the proposed solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantages over the existing approaches.

Query processing over uncertain data has gained growing attention, because it is necessary to deal with uncertain data in many real-life applications. In this paper, we investigate skyline queries over uncertain data in distributed environments (DSUD query) whose research is only in an early stage. The state-of-the-art algorithm, called e-DSUD algorithm, is designed for processing this query. It has the desirable characteristics of progressiveness and minimum bandwidth consumption. However, it still needs to be perfected in three aspects. (1) Progressiveness. Each time it only returns one query result at most. (2) Efficiency. There are a significant amount of redundant I/O cost and numerous iterations which causes a long total query time. (3) Universality. It is restricted to the case where local skyline tuples are incomparability. To address these concerns, we first present a detailed analysis of the e-DSUD algorithm and then develop an improved framework for the DSUD query, namely IDSUD. Based on the new framework, we propose an adaptive algorithm, called ADSUD, for the DSUD query. In the algorithm, we redefine the approximate global skyline probability and choose local representative tuples due to minimum probabilistic bounding rectangle adaptively. Furthermore, we design a progressive pruning method and apply the reuse mechanism to improve its efficiency. The results of extensive experiments verify the better overall performance of our algorithm than the e-DSUD algorithm.

In this study, we focus on how to extract role-explicit queries from a Chinese query log. A framework that performs role-explicit query extraction concurrently with intent role annotation is proposed. Instead of independently processing each query, the human wisdom hidden in mul-sessions is deployed to quantify the certainty of a word being the kernel-object. A sliding-window algorithm is proposed to extract role-explicit queries per mul-session. To filter the unreliable results produced by this algorithm, a pattern-based algorithm that performs a global purification is designed. The entire framework enables us to obtain a repository of sufficient role-explicit queries from a query log without human intervention. We also investigate the usefulness of aggregative role-explicit query extraction. Based a repository of role-explicit queries, we derive the richness value to quantify the richness of kernel-object oriented intents. An experimental application of the proposed framework for role-explicit query extraction to the query log SogouQ shows that: it achieves satisfactory performance. Furthermore, the richness value provides a way to capture underspecified and/or ambiguous queries, which allows selective operations to be performed depending on the nature of the queries.

An inconsistent database is a database instance violating integrity constraints. A repair of an inconsistent database is a maximal consistent subset. Sampling from the repair space is an alternative approach meeting the needs of many applications. In this paper, we introduce a new class of repair, query feedback restricted repair, based on the feedback on user's witness query. We first map out a picture of both data and combined complexities of repair existence problems under different cases to identify the intractable cases. Especially, we show that if the query is a projection or a union query, then the decision problem is NP-complete; even worse, if the query is a conjunctive query, the decision problem becomes Σ 2 P -complete. However, we prove that the combined complexity of the repair existence problem is in LOGSPACE when the witness query is a selection-join query, and this conclusion also implies that the combined complexity of side-effect free deletion propagation problem under group-deletion is in LOGSPACE which is not considered in previous works. Additionally, we provide a polynomial random repair sampling algorithm under combined complexity. At last, we revisit the key preserving condition 1 and show that it will simplify the problem, i.e., some cases become tractable for certain key preserving views, as opposed to their counterparts that are not key preserving.

We propose a unified approach for querying data sources which are accessible in form of heterogeneous APIs rather than some standards compliant databases. Different APIs use different query structures and support different capabilities. Type-theoretic approach is used for query structure representation. Because of the fact that not any API will support the full expressibility of the query language we propose techniques for “widening” the query to make it compatible with the given API possibilities. After the query is executed additional filters are applied to the acquired data. In order to achieve this goal a special set of query expansion rules was developed. It consists of the rules that generate special conversion functions which transform more specific queries into less specific ones.

Continuous $k$ -nearest neighbor (CkNN) query processing is an important issue in spatial temporal databases. In real-world scenarios, query clients and data objects may move with uncertain speeds on the road networks, which makes retrieving the exact CkNN query result a challenge. This paper addresses the issue of processing probabilistic CkNN queries of uncertain data (CPkNN) for road networks, where moving objects and query points are restricted by the connectivity of the road network and the object-query distance updates affect the query result. A novel model is proposed to estimate network distances between moving objects and a submitted moving query in the road network. Then, a CPkNN query monitoring method is presented to continuously report the possible result objects within a given time interval. In addition, an efficient method is proposed to arrange all the candidate objects according to their probabilities of being a kNN of a query. The method then chooses the top- $k$ objects as the final query result. In addition, we extend our method to large networks with high efficiency. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed schema.

Developing Natural Language Query Interface to Relational Databases has gained much interest in research community since forty years. This can be termed as structured free query interface as it allows the users to retrieve the data from the database without knowing the underlying schema. Structured free query interface should address majorly two problems. Querying the system with Natural Language Interfaces (NLIs) is comfortable for the naive users but it is difficult for the machine to understand. The other problem is that the users can query the system with different expressions to retrieve the same information. The different words used in the query can have same meaning and also the same word can have multiple meanings. Hence it is the responsibility of the NLI to understand the exact meaning of the word in the particular context. In this paper, a generic NLI Database system has proposed which contains various phases. The exact meaning of the word used in the query in particular context is obtained using ontology constructed for customer database. The proposed system is evaluated using customer database with precision, recall and f1-measure.

© 2018, Higher Education Press and Springer-Verlag GmbH Germany. Indexing is one of the most important techniques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is partially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a balanced tree-structured index in particular when datasets become larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a wellbalanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up manner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.

In this paper, we define a new class of queries, the top-k multiple-type integrated query (simply, top-k MULTI query). It deals with multiple data types and finds the information in the order of relevance between the query and the object. Various data types such as spatial, textual, and relational data types can be used for the top-k MULTI query. The top-k MULTI query distinguishes itself from the traditional top-k query in that the component scores to calculate final scores are determined dependent of the query. Hence, each component score is calculated only when the query is given for each data type rather than being calculated apriori as in the top-k query. As a representative instance, the traditional top-k spatial keyword query is an instance of the top-k MULTI query. It deals with the spatial data type and text data type and finds the information based on spatial proximity and textual relevance between the query and the object, which is determined only when the query is given. In this paper, we first define the top-k MULTI query formally and define a new specific instance for the top-k MULTI query, the top-k spatial-keyword-relational(SKR) query, by integrating the relational data type into the traditional top-k spatial keyword query. Then, we investigate the processing approaches for the top-k MULTI query. We discuss the scalability of those approaches as new data types are integrated. We also devise the processing methods for the top-k SKR query. Finally, through extensive experiments on the top-k SKR query using real and synthetic data sets, we compare efficiency of the methods in terms of the query performance and storage.

This paper explores an analysis-aware data cleaning architecture for a large class of SPJ SQL queries. In particular, we propose QuERy, a novel framework for integrating entity resolution (ER) with query processing. The aim of QuERy is to correctly and efficiently answer complex queries issued on top of dirty data. The comprehensive empirical evaluation of the proposed solution demonstrates its significant advantage in terms of efficiency over the traditional techniques for the given problem settings.

Important cloud services rely on spatial-keyword queries, containing a spatial predicate and arbitrary boolean keyword queries. In particular, we study the processing of such queries in main memory to support short response times. In contrast, current state-of-the-art spatial-keyword indexes and relational engines are designed for different assumptions. Rather than building a new spatial-keyword index, we employ a cost-based optimizer to process these queries using a spatial index and a keyword index. We address several technical challenges to achieve this goal. We introduce three operators as the building blocks to construct plans for main memory query processing. We then develop a cost model for the operators and query plans. We introduce five optimization techniques that efficiently reduce the search space and produce a query plan with low cost. The optimization techniques are computationally efficient, and they identify a query plan with a formal approximation guarantee under the common independence assumption. Furthermore, we extend the framework to exploit interesting orders. We implement the query optimizer to empirically validate our proposed approach using real-life datasets. The evaluation shows that the optimizations provide significant reduction in the average and tail latency of query processing: 7- to 11-fold reduction over using a single index in terms of 99th percentile response time. In addition, this approach outperforms existing spatial-keyword indexes, and DBMS query optimizers for both average and high-percentile response times.

We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over simple tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, GQBE (Graph Query By Example), automatically derives a weighted hidden maximal query graph based on input query tuples, to capture a user's query intent. It efficiently finds and ranks the top approximate answer tuples. For fast query processing, GQBE only partially evaluates query graphs. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

We present VIIQ (pronounced as wick), an interactive and iterative visual query formulation interface that helps users construct query graphs specifying their exact query intent. Heterogeneous graphs are increasingly used to represent complex relationships in schemaless data, which are usually queried using query graphs. Existing graph query systems offer little help to users in easily choosing the exact labels of the edges and vertices in the query graph. VIIQ helps users easily specify their exact query intent by providing a visual interface that lets them graphically add various query graph components, backed by an edge suggestion mechanism that suggests edges relevant to the user's query intent. In this demo we present: 1) a detailed description of the various features and user-friendly graphical interface of VIIQ , 2) a brief description of the edge suggestion algorithm, and 3) a demonstration scenario that we intend to show the audience.

The top- k query is a common means to shortlist a number of options from a set of alternatives, based on the user's preferences. Typically, these preferences are expressed as a vector of query weights, defined over the options' attributes. The query vector implicitly associates each alternative with a numeric score, and thus imposes a ranking among them. The top- k result includes the k options with the highest scores. In this context, we define the maximum rank query ( MaxRank ). Given a focal option in a set of alternatives, the MaxRank problem is to compute the highest rank this option may achieve under any possible user preference, and furthermore, to report all the regions in the query vector's domain where that rank is achieved. MaxRank finds application in market impact analysis, customer profiling, targeted advertising, etc. We propose a methodology for MaxRank processing and evaluate it with experiments on real and benchmark synthetic datasets.

The aim of the study was to test whether query expansion by approximate string matching methods is beneficial in retrieval from historical newspaper collections in a language rich with compounds and inflectional forms Finnish. First, approximate string matching methods were used to generate lists of index words most similar to contemporary query terms in a digitized newspaper collection from the 1800s. Top index word variants were categorized to estimate the appropriate query expansion ranges in the retrieval test. Second, the effectiveness of approximate string matching methods, automatically generated inflectional forms, and their combinations were measured in a Cranfield-style test. Finally, a detailed topic-level analysis of test results was conducted. In the index of historical newspaper collection the occurrences of a word typically spread to many linguistic and historical variants along with optical character recognition OCR errors. All query expansion methods improved the baseline results. Extensive expansion of around 30 variants for each query word was required to achieve the highest performance improvement. Query expansion based on approximate string matching was superior to using the inflectional forms of the query words, showing that coverage of the different types of variation is more important than precision in handling one type of variation.

Understanding the users' latent intents behind the search queries is critical for search engines. Hence, there has been an increasing attention on studying how to effectively mine the intents of search queries by analyzing search engine query log. However, we observe that the information richness of query log is not fully utilized so far and the information underuse heavily limits the performance of the existing methods. In this paper, we tackle the problem of query intent mining by taking full advantage of the information richness of query log from a multi-dimensional perspective. Specifically, we capture the latent relations between search queries via three different dimensions: the URL dimension, the session dimension and the term dimension. We first propose the Result-Oriented Framework (ROF), which is easy to implement and significantly improves both the precision and the recall of query intent mining. We further propose the Topic-Oriented Framework (TOF), in order to significantly reduce the online time and memory consumptions for query intent mining. TOF employs the Query Log Topic Model (QLTM) that derives the latent topics from query log to integrate the information of the three dimensions in a principled way. The latent topics that are considered as low-dimensional descriptions of the query relations and serve as the basis of efficient online query intent mining. We conduct extensive experiments on a major commercial search engine query log. Experimental results show that the two frameworks significantly outperform the state-of-the-art methods with respect to a variety of metrics.

Thus far query languages for graphs databases that, in addition to navigating the structure of a graph, also consider data values encountered along the paths they traverse, seem to exhibit somewhat dual behaviour in terms of the efficiency of their query evaluation problem. Namely, their combined complexity is either tractable, or are at least PSpace -hard. In this paper we show how to use the model of variable automata to get a query language with intermediate ( NP -complete) combined complexity of query evaluation. Since variable automata are incomparable in terms of expressive power with previously studied querying mechanisms for data graphs we also show how to join their capabilities with the ones of previously used languages without an increase in the complexity of query evaluation, thus getting the best of both worlds.

A database may for various reasons become inconsistent with respect to a given set of integrity constraints. In the late 1990s, the formal approach of consistent query answering was proposed in order to query such databases. Since then, a lot of efforts have been spent to classify the complexity of consistent query answering under various classes of constraints. It is known that for the most common constraints and queries, the problem is in coNP and might be coNP-hard, yet several relevant tractable classes have been identified. Additionally, the results that emerged suggested that given a set of key constraints and a conjunctive query, the problem of consistent query answering is either in PTime or is coNP-complete. However, despite all the work, as of today this dichotomy remains a conjecture. The main contribution of this article is to explain why it appears so difficult to obtain a dichotomy result in the setting of consistent query answering. Namely, we prove that such a dichotomy with respect to common classes of constraints and queries is harder to achieve than a dichotomy for the constraint satisfaction problem, which is a famous open problem since the 1990s.

In this paper, we propose a new three-hop information search algorithm in delay-tolerant networks. In this algorithm, information search is divided into two phases, query and feedback. In the query phase, the query's source user entrusts neighbors as query nodes to reach the purpose community and obtain the relevant information. In the feedback phase, every query node entrusts his neighbors as feedback nodes to give the information feedback to the query's source user. We apply a semi-Markov model to predict the user's future location. In the former phase, we select the nodes with higher query probabilities as the query delegations. Then the concept of first arrival time is defined here. In the latter phase, if the feedback probability of a neighbor node is higher than that of the query node, we will select it as a feedback node. We define this algorithm as query and feedback delegation(QFD). Through simulation, the results show that QFD not only improves the information query ratio but also reduces average delay for information query compared with the existing query schemes.

Modern day's queries are posed on database spread across the globe, this may impose a challenge on processing queries efficiently, and a strategy is required to generate optimal query plans. In distributed relational database systems, due to partitioning or replication on relations at multiple sites, the relations required by a query to answer, may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent alternatives or query plans for a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, the query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using parameter less optimization technique Teaching-Learner based Optimization (TLBO). The TLBO algorithm was observed to go one better than the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. Experimental comparisons of this algorithm with the multi-objective GA based distributed query plan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able to generate comparatively better quality Top-K query plans.

International audience; SPARQL query language plays a significant role in developing semantic web and web intelligence. In order to deal with large data query execution over RDF, SPARQL query optimizer is an essential component in the SPARQL query engine for improving query execution performance. This paper proposes an approach for performing query planning and optimization based on an extended query pattern graph and heuristics. First, this paper generalizes SPARQL query statement representation by taking other expressions into account, aiming at overcoming the limitations of only using basic query triple patterns. Second, this paper presents the heuristics for estimating the cost of executing query triple pattern. The proposed query planning methods are implemented within Corese query engine and are evaluated using BSBM benchmark. The results suggest that the proposed methods can optimize effectively the query execution time of SPARQL query engine.

The query of database must rely on professional query language, which is difficult to grasp for ordinary users. Natural language query interface (NLQI) can be applied to solve this problem with better human-computer interaction and intelligence. However, current natural language understanding technology is not mature. Though the query sentences’ structure is fixed, it is also difficult to accurately convert query sentences into understandable database query language for computer. Therefore a method of designing NLQI based on syntax and semantic analysis is proposed in this paper. First of all, Maximum forward and maximum reverse matching algorithms are used to analyse sentence by part of speech (POS) template, which is able to demonstrate the syntax relationship among the words. After syntax analysis, query conditions and targets are identified. Then, single conditions and targets are analyzed to relative standard forms by the relationship among entity, property, connect verb and unit. At last, these standard forms are transformed to SQL sentence. Regarding printing ERP system as experimental subject, experiment is done in this paper. The result shows that the method proposed in this paper can meet the needs of natural language query, and has a good transform effect for combination, omitting and other complex sentences.

In this paper, we define a novel type of skyline query, namely top-k combinatorial metric skyline (kCMS) query. The kCMS query aims to find k combinations of data points according to a monotonic preference function such that each combination has the query object in its metric skyline. The kCMS query will enable a new set of location-based applications that the traditional skyline queries cannot offer. To answer the kCMS query, we propose two efficient query algorithms, which leverage a suite of techniques including the sorting and threshold mechanisms, reusing technique, and heuristics pruning to incrementally and quickly generate combinations of possible query results. We have conducted extensive experimental studies, and the results demonstrate both effectiveness and efficiency of our proposed algorithms.

This paper proposes a new approach for approximate evaluation of #P-hard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME self-join-free conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over non-probabilistic methods for ranking query answers., Comment: 12 pages, 5 figures, pre-print for a paper appearing in VLDB 2015. arXiv admin note: text overlap with arXiv:1310.6257

Spatial object search is prevalent in map services (e.g., Google Maps). To rent an apartment, for example, one will take into account its nearby facilities, such as supermarkets, hospitals, and subway stations. Traditional keyword search solutions, such as the nearby function in Google Maps, are insufficient in expressing the often complex attribute/spatial requirements of users. Those require- ments, however, are essential to reflect the user search intention. In this paper, we propose the Spatial Exemplar Query (SEQ), which allows the user to input a result example over an interface inside the map service. We then propose an effective similarity measure to evaluate the proximity between a candidate answer and the given example. We conduct a user study to validate the effectiveness of SEQ. Our result shows that more than 88% of users would like to have an example assisted search in map services. Moreover, SEQ gets a user satisfactory score of 4.3/5.0, which is more than 2 times higher than that of a baseline solution.

During software maintenance, developers usually deal with a significant number of software change requests. As a part of this, they often formulate an initial query from the request texts, and then attempt to map the concepts discussed in the request to relevant source code locations in the software system (a.k.a., concept location). Unfortunately, studies suggest that they often perform poorly in choosing the right search terms for a change task. In this paper, we propose a novel technique --ACER-- that takes an initial query, identifies appropriate search terms from the source code using a novel term weight --CodeRank, and then suggests effective reformulation to the initial query by exploiting the source document structures, query quality analysis and machine learning. Experiments with 1,675 baseline queries from eight subject systems report that our technique can improve 71% of the baseline queries which is highly promising. Comparison with five closely related existing techniques in query reformulation not only validates our empirical findings but also demonstrates the superiority of our technique., The 32nd International Conference on Automated Software Engineering (ASE 2017), pp. 428-439, Urbana-Champaign, Illinois, USA, October, 2017

Queries are often ambiguous and can be interpreted in many ways, even by humans. Hence, semantic query understanding's primary objective is to understand the intention behind the query. This implies first predicting the language used to express the query. Second, parsing the query according to that language. Third, extracting the entities and concepts mentioned in the query. Finally, based on all this information, we predict one or more possible intentions with a certain probability, which is particularly important for ambiguous queries. These scores will be one of the inputs for the final semantic ranking. For example, given the query "bond", possible results for query understanding are a financial instrument, the movie character, a chemical reaction, or a term for endearment. Semantic ranking refers to ranking search results using semantic information. In a standard search engine, a rank is computed by using signals or features coming from the search query, from the documents in the collection being searched and from the search context, such as the language and device being used. Using semantic processing, we also add semantic features that come from concepts present in the knowledge base that appear in the query and semantically match documents in the collection. To do this efficiently, all documents are preprocessed semantically to build an index that includes semantic annotations. To accomplish semantic ranking, we use machine learning in several stages. The first stage selects the data sources that we should use to answer the query. In the second stage, each data source generates a set of answers using "learning to rank." The third and final stage ranks these data sources, selecting and ordering the intentions as well as the answers inside each intention (e.g., news) that will appear in the final composite answer. All these techniques are language independent, but may use language dependent features.

Query performance prediction has shown benefits to query optimization and resource allocation for relational databases. Emerging applications are leading to search scenarios where workloads with heterogeneous, structure-less analytical queries are processed over large-scale graph and network data. This calls for effective models to predict the performance of graph analytical queries, which are often more involved than their relational counterparts.In this paper, we study and evaluate predictive techniques for graph query performance prediction. We make several contributions. (1) We propose a general learning framework that makes use of practical and computationally efficient statistics from query scenarios and employs regression models. (2) We instantiate the framework with two routinely issued query classes, namely, reachability and graph pattern matching, that exhibit different query complexity. We develop modeling and learning algorithms for both query classes. (3) We show that our prediction models readily apply to resource-bounded querying, by providing a learning-based workload optimization strategy. Given a query workload and a time bound, the models select queries to be processed with a maximized query profit and a total cost within the bound. Using real-world graphs, we experimentally demonstrate the efficacy of our framework in terms of accuracy and the effectiveness of workload optimization.

This paper investigates the problem of reverse engineering, i.e., learning, select-project-join (SPJ) queries from a user-provided example set, containing positive and negative tuples. The goal is then to determine whether there exists a query returning all the positive tuples, but none of the negative tuples, and furthermore, to find such a query, if it exists. These are called the satisfiability and learning problems, respectively. The ability to solve these problems is an important step in simplifying the querying process for non-expert users.This paper thoroughly investigates the satisfiability and learning problems in a variety of settings. In particular, we consider several classes of queries, which allow different combinations of the operators select, project and join. In addition, we compare the complexity of satisfiability and learning, when the query is, or is not, of bounded size. We note that bounded-size queries are of particular interest, as they can be used to avoid over-fitting (i.e., tailoring a query precisely to only the seen examples).In order to fully understand the underlying factors which make satisfiability and learning (in)tractable, we consider different components of the problem, namely, the size of a query to be learned, the size of the schema and the number of examples. We study the complexity of our problems, when considering these as part of the input, as constants or as parameters (i.e., as in parameterized complexity analysis). Depending on the setting, the complexity of satisfiability and learning can vary significantly. Among other results, our analysis also provides new problems that are complete for W[3], for which few natural problems are known. Finally, by considering a variety of settings, we derive insight on how the different facets of our problem interplay with the size of the database, thereby providing the theoretical foundations necessary for a future implementation of query learning from examples.

The proliferation of geo-Textual data gives prominence to spatial keyword search. The basic top-k spatial keyword query, returns k geo-Textual objects that rank the highest according to their textual relevance and spatial proximity to query keywords and a query location. We define, study, and provide means of computing the reverse top-k keyword-based location query. This new type of query takes a set of keywords, a query object q, and a number k as arguments, and it returns a spatial region such that any top-k spatial keyword query with the query keywords and a location in this region would contain object q in its result. This query targets applications in market analysis, geographical planning, and location optimization, and it may support applications related to safe zones and influence zones that are used widely in location-based services. We show that computing an exact query result requires evaluating and merging a set of weighted Voronoi cells, which is expensive. We therefore devise effective algorithms that approximate result regions with quality guarantees. We develop novel pruning techniques on top of an index, and we offer a series of optimization techniques that aim to further accelerate query processing. Empirical studies suggest that the proposed query processing is efficient and scalable.

Promoting declarative approaches in data mining is a long standing theme, the main idea being to simplify as much as possible the way data analysts interact with their data. This paper goes into this direction by proposing a well-founded logical query language, S a f e RL , allowing the expression of a wide variety of rules to be discovered against a database. By rules, we mean statements of the form “if …then …”, as defined in logics for “implications” between boolean variables. As a consequence, S a f e RL extends and generalizes functional dependencies to new and unexpected rules. We provide a query rewriting technique and a constructive proof of the main query equivalence theorem, leading to an efficient query processing technique. From S a f e RL , we have devised RQL, a user-friendly SQL-like query language. We have shown how a tight integration can be performed on top of any relational database management system. Every RQL query turns out to be seen as a query processing problem, instead of a particular rule mining problem. This approach has been implemented and experimented on sensor network data. A web prototype has been released and is freely available ( http://rql.insa-lyon.fr ). Data analysts can upload a sample of their data, write their own RQL queries and get answers to know whether or not a rule holds (if not, a counterexample from the database is displayed) and much more.

Proliferation of RDF data has reached to a peak where data is partitioned across multiple nodes. Significant contribution for developing solutions to manage RDF data in distributed environment is witnessed in recent years. We propose a workload aware hybrid partitioning approach for a distributed environment. The objective of our approach is reducing query joins and inter-node communication leading it to faster query execution for frequent queries. Our approach considers a query workload and partitions data based on workload information. It distributes data by exploiting underlying structural relationship between properties using a property reachability matrix to optimize query performance. DWAHP gets rid of inter-node communication cost for frequent queries like linear and star queries and answers 83% of frequent query workload without inter-node communication. DWAHP is compared with state-of-the-art solutions in terms of query execution time, query cost, storage space, and inter-node communication. It has demonstrated significant improvement over state-of-the-art solution.

To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine.

Keyword query on RDF data is an effective option because it is lightweight and it is not necessary to have prior knowledge on the data schema or a formal query language such as SPARQL. However, optimizing the query processing to produce the most relevant results with only minimum computations is a challenging research issue. Current proposals suffer from several drawbacks, e.g., limited scalability, tight coupling with the existing ontology, and too many computations. To address these problems, we propose a novel approach to keyword search with automatic depth decisions using the relational and semantic similarities. Our approach uses a predicate that represents the semantic relationship between the subject and object. We take advantage of this to narrow down the target RDF data. The semantic similarity score is then calculated for objects with the same predicate. We make a linear combination of two scores to get the similarity score that is used to determine the depth of given keyword query results. We evaluate our algorithm with other approaches in terms of accuracy and query processing performance. The results of our empirical experiments show that our approach outperforms other existing approaches in terms of efficiency and query processing performance.