Understanding Search Engines: Mathematical Modeling and Text Retrieval (2nd Edition)

Understanding search engines - mathematical modeling and text retrieval, Second Edition

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Repair localization for query answering from inconsistent databases by Eiter et al. Preference-driven querying of inconsistent relational databases by Staworko et al.

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A Logic programming approach to the integration, repairing and querying of inconsistent databases by Greco et al. OLAP over uncertain and imprecise data by Burdick et al. Using constraint satisfaction for view update by Hua Shu in J.

Monotonic complements for independent data warehouses by Laurent et al. On the computation of relational view complements by Lechtenb? The impact of the constant complement approach towards view updating by Lechtenb? On propagation of deletions and annotations through views by Buneman et al. Updates of relational views by Cosmadakis and Papadimitriou in J. ACM 31 4 , Run-time translation of view tuple deletions using data lineage by Cui and Widom, Tech.

Report Stanford Univ. Update and retrieval in a relational database through a universal schema interface by Brosda and Vossen in TODS 13 4 Updates through views: A new hope by Kotidis et al.

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Relational lenses: a language for updatable views by Bohannon et al. Ilyas, George Beskales, and Mohamed A. Soliman To appear in the ACM Computing Surveys, Top-k selection queries over relational databases: mapping strategies and performance evaluation by Bruno et al.

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Optimal Aggregation Algorithms for Middleware by Fagin et al. See also the journal version in J. On saying "Enough Already! Supporting top-k join queries in relational databases. Data Cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals by Jim Gray et al. Data Mining and Knowledge Discovery, 1 1 , An array-based algorithm for simultaneous multidimensional aggregates by Zhao et al.

Fundamentals of Data Warehouses 2nd Ed. Mendelzon, C. Hurtado, and D. Lemire View Selection Materialized views selection in a multidimensional database. The view selection problem has an exponential time lower bound for conjunctive queries and views. Materialized view selection for multidimensional datasets by Shukla et al. Implementing data cubes efficiently by Harinarayan et al. Algorithms for materialized view design in data warehousing environment by Yang et al. View selection using randomized search by Kalnis et al.

Caching multidimensional queries using chunks by Deshpande et al. Semantic data caching and replacement by Dar et al. DynaMat: a dynamic view management system for data warehouses. Exact and inexact methods for selecting views and indexes for OLAP performance improvement by Talebi et al. A view selection algorithm with performance guarantee by Hanusse et al. Towards materialized view selection for distributed databases by Chaves et al.

Maintenance of data cubes and summary tables in a warehouse by Mumick et al. View maintenance in a warehousing environment by Zhuge et al. Materialized view maintenance and integrity constraint checking: trading space for time by Ross et al. Materialized Views in Oracle by Bello et al. Cubetree: organization of and bulk incremental updates on the data cube by Roussopoulos et al. On the computation of multidimensional aggregates by Agarwal et al.

Computing iceberg queries eficiently by Fang et al. Condensed cube:an effective approach to reducing data cube size by Wang et al. Dwarf: shrinking the PetaCube by Y. Sismanis et al. Logical foundations of peer-to-peer data integration by Calvanese et al. On reconciling data exchange, data integration, and peer data management by De Giacomo et al. Hyperion Project at U. Stream Data Management by Chaudhry et al. Stream Data Processing by Chakravarthy and Jiang ed.

Building a Search Engine with BERT and TensorFlow

Models and issues in data stream systems by Babcock et al. Issues in data stream management by Golab and?

TeleGraphCQ project at Berkley. Borealis , successor of Aurora. Mining of Massive Datasets by Rajaraman and Ullman.

ACM 23 4 , The Art of Prolog, 2nd Ed. By Shapiro and Sterling. Cambridge, Programming in Prolog, 5th Ed. By Clocksin and Mellish, Springer, The Craft of Prolog. From Logic Programming to Prolog. By Apt, Prentice Hall, Foundations of Logic Programming 2nd Ed. Learn Prolog Now. By Blackburn et al. Logic, Programming and Prolog 2nd Ed. Online book under GNU. Page et al. Report, Stanford Univ. Langville and Carl D. Meyer, Princeton University Press, Managing Gigabytes by Witten et al. ACM 51 1 : The Web as a Graph by Kumar et al.

See also The web as a graph: measurements, models, and methods by Kleinberg et al. Map-reduce-merge: simplified relational data processing on large clusters by Yang et al. Zobel and A. Open Source Projects such as Lucene , Egothor , and Nutch Web Service Composition The following is the content of literature research of an abandoned thesis topic for a master student.

Stonebraker et al. See also C-Store Project Home. ACM 24, 7 Jul. Semantic database modeling: survey, applications, and research issues. The stable model semantics for logic programming by M. Gelfond and V. It emphasizes the important roles that applied mathematics can play in improving information retrieval.

The authors discuss not only important data structures, algorithms and software, but also user-centred issues such as interfaces, manual indexing, and document preparation. The authors bridge the gap between applied mathematics and information retrieval. View PDF. Save to Library. In other words, terms that are frequent in a given document and infrequent in the whole collection are assigned high TF-IDF weight. TF-IDF is generally defined as: wij is the final weight for term j, tfij is the frequency of term j in document i, and idfj is the Inverse Document Frequency of term j.

TF-IDF is by far the most successful document term weighting scheme and is applicable to almost all vector space information retrieval systems Salton and Buckley, Entropy Term Weighting The Entropy weighting scheme assigns a weight of 0 for terms that appear once in every document, and a weight of 1 for terms that appears once in one document, and a weight between 0 and 1 for other combination of frequencies. The advantage of Entropy weighting is that it takes into consideration the distribution of terms over the collection by assigning higher weight for terms that occur less in a small number of documents Dumais, In essence, SWVM is based on the Vector Space Model VSM originally proposed by Salton, Wong, and Yang , however, adjusted to exploit hypertext language in a way not to treat a web document as a regular classical unstructured text, but to take into account its tag structure and significantly boosts the weight of terms that appear in certain tags that are associated with the semantics of the document.

Figure 1: Document modeling phase 3. Synonyms are generated using a thesaurus during the indexing phase and stored together with other terms. The use of synonyms strengthens the retrieval system and allows a higher query-document match, especially when a query has similar context similar meaning with respect to a document but different vocabulary terms Garcia, Basically, all synonyms for a particular term ti are assigned a weight equal to the weight of the initial term ti.

The proposed SWVM considers the synonyms for every term it models. It utilizes a compiled thesaurus to assist in the generation of synonyms for every term during the indexing phase.

Initializations for the Nonnegative Matrix Factorization (2006)

A thesaurus is usually a list of words organized according to their similarities, differences, and other linguistic relationships. Put differently, it weights terms according to the tags in which they appear.

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