Download Advances in Data Management by Piotr Kołaczkowski, Henryk Rybiński (auth.), Zbigniew W. PDF

By Piotr Kołaczkowski, Henryk Rybiński (auth.), Zbigniew W. Ras, Agnieszka Dardzinska (eds.)

Data administration is the method of making plans, coordinating and controlling
data assets. extra usually, purposes have to shop and seek a wide
amount of information. dealing with information has been continually challenged
by calls for from numerous components and purposes and has advanced in parallel
with advances in and computing ideas.

This quantity makes a speciality of its fresh advances and it truly is composed of 5 parts
and a complete of eighteen chapters. the 1st a part of the e-book includes 5
contributions within the sector of data retrieval & net intelligence: a singular
approach to fixing index choice challenge, built-in retrieval from net of
documents and information, bipolarity in database querying, deriving information summarization
through ontologies, and granular computing for internet intelligence.
The moment a part of the publication comprises 4 contributions in wisdom discovery zone.
Its 3rd half includes 3 contributions in details integration & information
security region. the rest elements of the ebook comprise six contributions
in the world of clever brokers and purposes of knowledge administration
in scientific domain.

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X ]) QUERY: getDocsByBindingsAndContent: Single-Var-Triples-list x PowerSet(STRINGS) -> PowerSet(DN) ABBREVIATION FOR: getDocsByBindingsAndContent(vtl, kws) = getBindings (vtl) ∧ getDocsByKeywords(kws) where vtl ∈ Single-Var-Triples-list, kws ∈ PowerSet(STRINGS). SEMANTICS: This query retrieves document nodes that match the keywords and contain the matching triples. x] “Semantic Grid”) 5 Implementation and Results We have implemented an Apache Lucene [11] based retrieval system called SITAR (Semantic InformaTion Analysis and Retrieval system) based upon our model [6].

Data Retrieval techniques are typically used to retrieve data from the Semantic Web while Information Retrieval techniques are used to retrieve documents from the Hypertext Web. We present a Unified Web model that integrates the two webs and formalizes connection between them. We then present an approach to retrieving documents and data that captures best of both the worlds. Specifically, it improves recall for legacy documents and provides keyword-based search capability for the Semantic Web. We specify the Hybrid Query Language that embodies this approach, and the prototype system SITAR that implements it.

Id_db=20 QUERY: Nodes-ref::=ss, where ss ∈ PowerSet(STRINGS). ANSWER: Result(ss) = { n in N | ss ⊆ IndexWords(n) } SEMANTICS: The wordset query, ss, usually written as a set of strings delimited using angular brackets, returns the set of nodes whose IndexWords contain ss. EXAMPLE: 34 K. Thirunarayan and T. Immaneni Nodes-ref ::= pp::ss, where pp, ss ∈ PowerSet(STRINGS). Result(pp::ss) = { n ∈ N | ss ⊆ IndexWords(n) ∧ ∃m : n ISA m ∧ pp ⊆ IndexWords(m) } SEMANTICS: The wordset-pair query, pp::ss, usually written as two wordsets delimited using colon, returns the set of nodes such that each node has IndexWords that contains ss and has an ISA ancestor whose IndexWords contains pp.

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