By Don Chamberlin

DB2 common Database (UDB) helps many differing kinds of purposes, on many alternative types of info, in lots of diverse software program and environments.

This e-book offers an entire consultant to DB2 UDB model five in all its facets, together with the interfaces that help finish clients, software builders, and database directors. it really is complementary to the IBM product documentation, offering a transparent and casual clarification of the way the positive factors of DB2 have been meant for use. it truly is an in depth revision of the author's past booklet, utilizing the recent DB2: IBM's Object-Relational Database method . * deals entire and self-contained info, and doesn't suppose earlier wisdom of DB2, SQL, or relational database recommendations * Covers ordinary ideas of database administration in addition to the complicated good points of UDB, together with recursive queries, constraints, triggers, user-defined datatypes, saved techniques, parallel databases, and graphical instruments for database management * contains dozens of functional suggestions that would keep readers many hours of labor in constructing database purposes * presents countless numbers of demonstrated examples written in SQL, C, C++, and Java, all of that are to be had at the MKP site

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Consequently, the method proposed here for technical recommendations can be easily modified for fundamental recommendations. 1 The Nature of Stock Recommendations Technical recommendations are generally in free-text forms as depicted in Fig. 3. One notable feature is that one article may contain several recommendations and there is no specified delimiters for separating one recommendation from the others. Additionally, the pattern that each analyst used to present their recommendations can be very different.

However, in order to simplify the presentation, we have assumed only one attribute for describing documents in this paper, namely Content description. As a consequence, we omit the attribute name in the description of a document and give only the set of attribute values. Moreover we have assumed that the values of this unique attribute are organized in a taxonomy. Extending our results to descriptions involving more than one attribute is rather straightforward: instead of having single attribute values we will have tuples of values, and what needs to be done then is to define a subsumption Authoring Composite Documents and Their Descriptions 23 relation over tuples based on the subsumption relations over the individual attribute values.

Definition 8 (Refinement relation). Let D1 and D2 be two descriptions. We say that D1 is finer than D2 , denoted D1 ≡ D2 , iff ∀t2 ∈ D2 , ∃t1 ∈ D1 ∧ t1 t2 . For example, Dsum is finer than Dsum , as for every term t in Dsum , we can Algorithms and OOL find a term in Dsum subsumed by t. Indeed, Sort Languages. The refinement relation ≡ is clearly reflexive and transitive. Moreover, over reduced descriptions, ≡ becomes antisymmetric. From these properties we can say that ≡ is a partial order over reduced descriptions (see [3] for more details).

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