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Session search, the task of document retrieval for a series of queries in a session, has been receiving increasing attention from the information retrieval research community. Session search exhibits the properties of rich user-system interactions and temporal dependency. These properties lead to our proposal of using partially observable Markov decision process to model session search. On the basis of a design choice schema for states, actions and rewards, we evaluate different combinations of these choices over the TREC 2012 and 2013 session track datasets. According to the experimental results, practical design recommendations for using PODMP in session search are discussed. [ABSTRACT FROM AUTHOR]

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 comparison, prior IR systems fail to resolve any queries. [ABSTRACT FROM AUTHOR]

In recent years the inverted lists evaluation model along with holistic stack-based algorithms have been established as the most prominent techniques for evaluating XML queries on large persistent XML data. In this framework, we are using materialized views for optimizing XML queries. We consider a novel approach which instead of materializing the answer of a view materializes exactly the inverted sublists that are necessary for computing the answer of the view. This originality allows storing view materializations as compressed bitmaps, a solution that minimizes the materialization space and empowers performing optimization operations as CPU-efficient bitwise operations. To realize the potential of bitmap materialized views in optimizing query performance, we define and address the following problem (view configuration problem): given an XML tree and its schema find a template of tree-pattern views (view configuration) such that: (a) the views of this configuration can answer all the queries that can be issued against the schema, (b) their materialization fits in the space provided, and (c) evaluating the queries using these views minimizes the overall query evaluation cost. We consider an instance of this problem for tree pattern queries. Our intension is to find view configurations whose materializations are small enough to be stored in main memory. We find two candidate solution configurations and we identify cases where views can be excluded from materialization in a configuration without affecting query performance. In order to compare our approach with an approach which also can support the optimization of every query on the schema, we implemented an improvement of a state-of-the-art approach which is based on structural indexes. Our experimental results show that our approach is stable, greatly improves evaluating queries without materialized views, outperforms the structural index approach on all test cases and is very close to the optimal. These results characterize our approach as the best candidate for supporting the optimization of queries in the framework of the inverted lists model. [ABSTRACT FROM AUTHOR]

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Term-based approaches are used to extract a large number of features, most of which are meaningless noise, and suffer from polysemy and synonymy. Most popular text mining techniques consider the distribution of terms in documents and data sets when calculating the weight of the terms. The innovative technique presented in this paper is called Specific Feature Discovery and is uses positive feedback only to discover positive patterns as high-level features to consider relationship among terms, then high-level patterns deployed to low-level terms to overcome the limitation of patterns. To improve the quality of the extracted features, high-level patterns are extracted from feedback documents. Following this, low-level terms extracted from high-level patterns. Determining the weight of the low-level terms is based on the distribution of the terms in the high-level patterns and the specificity of the terms in a positive group of documents. The specificity of the low-level terms is calculated according to the frequency of the terms in the documents and the frequency of the terms in extracted patterns. There have been extensive experiments in which this technique has been tested using the Reuters Corpus Volume 1 data set and Text REtrieval Conference topics. The results show that the proposed method achieves an encouraging performance in all measures. [ABSTRACT FROM AUTHOR]

We present an approach to query modeling that leverages the temporal distribution of documents in an initially retrieved set of documents. In news-related document collections such distributions tend to exhibit bursts. Here, we define a burst to be a time period where unusually many documents are published. In our approach we detect bursts in result lists returned for a query. We then model the term distributions of the bursts using a reduced result list and select its most descriptive terms. Finally, we merge the sets of terms obtained in this manner so as to arrive at a reformulation of the original query. For query sets that consist of both temporal and non-temporal queries, our query modeling approach incorporates an effective selection method of terms. We consistently and significantly improve over various baselines, such as relevance models, on both news collections and a collection of blog posts. [ABSTRACT FROM AUTHOR]

There is no consensus as to whether a Liar sentence is meaningful or not. Still, a widespread conviction with respect to Liar sentences (and other ungrounded sentences) is that, whether or not they are meaningful, they are useless. The philosophical contribution of this paper is to put this conviction into question. Using the framework of assertoric semantics, which is a semantic valuation method for languages of self-referential truth that has been developed by the author, we show that certain computational problems, called query structures, can be solved more efficiently by an agent who has self-referential resources (amongst which are Liar sentences) than by an agent who has only classical resources; we establish the computational power of self-referential truth. The paper concludes with some thoughts on the implications of the established result for deflationary accounts of truth. [ABSTRACT FROM AUTHOR]

Smoothing of document language models is critical in language modeling approaches to information retrieval. In this paper, we present a novel way of smoothing document language models based on propagating term counts probabilistically in a graph of documents. A key difference between our approach and previous approaches is that our smoothing algorithm can iteratively propagate counts and achieve smoothing with remotely related documents. Evaluation results on several TREC data sets show that the proposed method significantly outperforms the simple collection-based smoothing method. Compared with those other smoothing methods that also exploit local corpus structures, our method is especially effective in improving precision in top-ranked documents through ''filling in'' missing query terms in relevant documents, which is attractive since most users only pay attention to the top-ranked documents in search engine applications. [ABSTRACT FROM AUTHOR]

A spatial query language called SQL/Spatial has been designed to support the expression of user requests dealing with a variety of GIS analysis functions (e.g., map overlay and buffer) in the Web environment. Using the familiar SELECT - FROM - WHERE statement instead of procedural commands like macro language in some GIS packages, users can formulate their queries more easily, while conducting powerful spatial data retrieval and analysis. The language investigates an appropriate structure to express spatial queries within the framework of the on-going SQL standards for spatial extensions (i.e., Open GIS Simple Features Specification for SQL and SQL3 MultiMedia Specification Part 3: Spatial). By restructuring the FROM clause via a subquery, SQL/Spatial is well adapted to the general spatial analysis procedure by using current GIS packages, while conforming well to the SQL design concepts. The interface (client) of the language resides with a Web browser, while the query is processed by a query server. The query server, linked to the Web server via Common Gateway Interface (CGI) scripts, is composed of a query processor, Spatial Database Engine (SDE) and MapObjects Internet Map Server. It was demonstrated that the design of SQL/Spatial facilitates the access to spatial analysis functionalities on the Web. [ABSTRACT FROM AUTHOR]