An edited volume containing data structures and algorithms for information retrieved including a disk with examples written in C. For programmers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents.

https://hornad.fei.tuke.sk/~genci/Vyucba/OOBDS/Archiv/Podklady/TExtoveIS&IR/Information%20Retrieval%20Data%20Structures%20&%20Algorithms.PDF

Information Retrieval: Data Structures and Algorithms

Datalog, a database query language based on the logic programming paradigm, is described. The syntax and semantics of Datalog and its use for querying a relational database are presented. Optimization methods for achieving efficient evaluations of Datalog queries are classified, and the most relevant methods are presented. Various improvements of Datalog currently under study are discussed, and what is still needed in order to extend Datalog's applicability to the solution of real-life problems is indicated. >

What you always wanted to know about Datalog (and never dared to ask)

Logic and databases have gone a long way together since the advent of relational databases. Already the first basic query languages for relational databases beside relational algebra — tuple calculus and domain calculus — are actually a subset of first order predicate logic. Furthermore logic proved to be very adequate for establishing a sound theory for relational databases.

Logic and databases

The area of deductive databases has matured in recent years, and it now seems appropriate to reflect upon what has been achieved and what the future holds. In this paper, we provide an overview of the area, with a focus on implementation techniques, and briefly describe a number of projects that have led to implemented systems.

A survey of deductive database systems

In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently. In this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A filtering system, SIFTER, has been implemented based on the model, using established techniques in information retrieval and artificial intelligence. These techniques include document representation by a vector-space model, document classification by unsupervised learning, and user modeling by reinforcement learning. The system can filter information based on content and a user's specific interests. The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents. We also describe experimental studies conducted with SIFTER to filter computer and information science documents collected from the Internet and commercial database services. The experimental results demonstrate that the system performs very well in filtering documents in a realistic problem setting.

A multilevel approach to intelligent information filtering: model, system, and evaluation

Based on matured database technology the paper provides new insights into efficient ways to evaluate recursive deduction rules. We show how the forward-chaining approach to deduction can flexibly be married with goal-directed aspects of best/easiest-first strategies. From the natural fixpoint semantics of recursion we develop generally applicable differential iteration schemes that efficiently compute the fixpoint. Surprisingly the well-known Warshall-algorithm gets disclosed as a descendant of this class of algorithms. Performance measurements suggest the former as well as systolic Δ-algorithms with linear fixpoint equation as candidates for incorporating a transitive closure operator in databases. As a next important step towards integration of database technology and logic programming we suggest to profit from the standard features of concurrency control and transaction management by effectively using them for the synchronization of parallel deductions.

On the evaluation of recursion in (deductive) database systems by efficient differential fixpoint iteration

Users of information retrieval systems (IRS) know and use many relationships between concepts a long time before these find their way into textbooks, printed thesauri, or classification schemes. We present here an IRS component called TEGEN, which taps this expertise by automatically drawing conclusions from actual search behavior about possible thesaurus entries. This is done during an iterative knowledge acquisition process: only after explicit or implicit confirmation by other users of the IRS during the knowledge verification process, the results are incorporated into a thesaurus. TEGEN is written in PASCAL using a knowledge-based programming method. It uses the relational database system IMF2 and is implemented at the Technical University of Munich and at the Leibniz Computer Center of the Bavarian Academy of Sciences.

Automatic thesaurus construction by machine learning from retrieval sessions

Automatic Thesaurus Construction by Machine Learning from Retrieval Sessions.

After about two decades since the origin of relational database technology its benefits and shortcomings are well recognized in academics and in the market place. Many efforts have been focused during the past five years on extending current relational database technology towards deductive databases which offer a more powerful reasoning capability based on Horn clause logic; i.e. perform exact reasoning. Due to the ubiquity of uncertain knowledge in real-life applications, the integration of uncertainty in deductive databases is highly desirable, too, and led to so-called quantitative deductive databases very recently. As this integration can be achieved in a natural way preserving the foundations of fixpoint semantics and relational algebra, established query optimization techniques can be taken over directly. This paper focuses on how to integrate the popular magic methods for optimizing recursion in the quantitative deductive database framework. We describe two refined methods, the QMagic Set method and the Supplementary QMagic Set method. Our performance results for a series of benchmarks demonstrate their increased efficiency compared to the standard magic methods. As a specialty we show how to efficiently implement certainty-guided deduction by Yo-Yo iteration.

Combining Deduction by Certainty with the Power of Magic

Skyline queries have recently received a lot of attention due to their intuitive query formulation: users can state preferences with respect to several attributes. Unlike numerical preferences, preferences over discrete value domains do not show an inherent total order, but have to rely on partial orders as stated by the user. In such orders typically many object values are incomparable, increasing the size of skyline sets significantly, and making their computation expensive. In this paper we explore how to enable interactive tasks like query refinement or relevance feedback by providing ‘prime cuts’. Prime cuts are interesting subsets of the full Pareto skyline, which give users a good overview over the skyline. They have to be small, efficient to compute, suitable for higher numbers of query predicates, and representative. The key to improved performance and reduced result set sizes is the relaxation of Pareto semantics to the concept of weak Pareto dominance. We argue that this relaxation yields intuitive results and show how it opens up the use of efficient and scalable query processing algorithms. Assessing the practical impact, our experiments show that our approach leads to lean result set sizes and outperforms Pareto skyline computations by up to two orders of magnitude.

/pdf/getting-prime-cuts-from-skylines-over-partially-ordered-2mjivg8jzz.pdf

Ulrich Güntzer

Papers

On the evaluation of recursion in (deductive) database systems by efficient differential fixpoint iteration

Automatic thesaurus construction by machine learning from retrieval sessions

Automatic Thesaurus Construction by Machine Learning from Retrieval Sessions.

Combining Deduction by Certainty with the Power of Magic

Getting Prime Cuts from Skylines over Partially Ordered Domains.