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11
Automated Database Schema Design Using Mined Data Dependencies
 J. Amer. Soc. Inform. Sci
, 1998
"... Data dependencies are used in database schema design to enforce the correctness of a database as well as to reduce redundant data. These dependencies are usually determined from the semantics of the attributes and are then enforced upon the relations. This paper describes a bottomup procedure for d ..."
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Data dependencies are used in database schema design to enforce the correctness of a database as well as to reduce redundant data. These dependencies are usually determined from the semantics of the attributes and are then enforced upon the relations. This paper describes a bottomup procedure for discovering multivalued dependencies (MVDs) in observed data without knowing `a priori the relationships amongst the attributes. The proposed algorithm is an application of the technique we designed for learning conditional independencies in probabilistic reasoning. A prototype system for automated database schema design has been implemented. Experiments were carried out to demonstrate both the effectiveness and efficiency of our method. 1
Granular Computing: A Problem Solving Paradigm
, 2005
"... Granulation is a natural problemsolving methodology deeply rooted in human thinking; it is intrinsically fuzzy, vague and imprecise. Mathematicians idealized it to partition, and developed into a fundamental problem solving methodology. Granulation and partition are examined in parallel from the p ..."
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Granulation is a natural problemsolving methodology deeply rooted in human thinking; it is intrinsically fuzzy, vague and imprecise. Mathematicians idealized it to partition, and developed into a fundamental problem solving methodology. Granulation and partition are examined in parallel from the prospect of problem solving. In partition theory, knowledge processing are transformed into table or tree processing. For general granulation such transformation is not there. In this paper, we take a new fresh look at previous results [18], including the recent applications in computer security (Chinese Wall Security Policy model) from this prospect; the knowledge processing have been transformed to table and tree processing in the (pre)topological setting.
The Relational Database Theory of Bayesian Networks
, 2000
"... Based on the elegant theory of relational databases, the present investigation establishes a unified model for both relational databases and Bayesian networks. This is in contradiction to the argument that relational databases and Bayesian networks are different, where it was shown that the implicat ..."
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Based on the elegant theory of relational databases, the present investigation establishes a unified model for both relational databases and Bayesian networks. This is in contradiction to the argument that relational databases and Bayesian networks are different, where it was shown that the implication problem does not coincide for embedded multivalued dependency (EMVD) and probabilistic conditional independence (CI). The main result of this thesis, however, is that the implication problem coincides on the solvable subclasses of EMVD and CI, but differs on the unsolvable general classes of EMVD and CI. This means that there is no practical difference between relational databases and Bayesian networks, since only the solvable subclasses are useful in the design of both of these knowledge systems.
Granular Computing II: Infrastructures for AIEngineering
, 2006
"... What is granular computing? There are no well accepted formal definitions yet. Informally, any computing theory/technology that involves elements and granules (subsets or generalized subsets) may be called granular computing (GrC). Intuitively, elements are the data, and granules are the basic knowl ..."
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What is granular computing? There are no well accepted formal definitions yet. Informally, any computing theory/technology that involves elements and granules (subsets or generalized subsets) may be called granular computing (GrC). Intuitively, elements are the data, and granules are the basic knowledge. So granular computing is the infrastructures for AIEngineering: uncertainty management, data mining, knowledge engineering, and learning.
Mining Associations by Linear Inequalities
"... The main theorem is: Generalized associations of a relational table can be found by a finite set of linear inequalities within polynomial time. It is derived from the following three results, which were established in ICDM0’02 and are redeveloped here. They are (1) Isomorphic Theorem: Isomorphic re ..."
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The main theorem is: Generalized associations of a relational table can be found by a finite set of linear inequalities within polynomial time. It is derived from the following three results, which were established in ICDM0’02 and are redeveloped here. They are (1) Isomorphic Theorem: Isomorphic relations have isomorphic patterns. Such an isomorphism classifies relational tables into isomorphic classes. (2) A variant of the classical bitmaps indexes uniquely exists in each isomorphic class. We take it as the canonical model of the class. (3) All possible attributes/features can be generated by a generalized procedure of the classical AOG (attribute oriented generalization). Then, (4) the main theorem for canonical model is established. By isomorphism theorem, we had the final result (5).
Attribute (Feature) Completion – The Theory of Attributes from Data Mining Prospect
"... A ”correct ” selection of attributes (features) is vital in data mining. As a first step, this paper constructs all possible attributes of a given relation. The results are based on the observations that each relation is isomorphic to a unique abstract relation, called canonical model. The complete ..."
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A ”correct ” selection of attributes (features) is vital in data mining. As a first step, this paper constructs all possible attributes of a given relation. The results are based on the observations that each relation is isomorphic to a unique abstract relation, called canonical model. The complete set of attributes of the canonical model is, then, constructed. Any attribute of a relation can be interpreted (via isomorphism) from such a complete set.
Granular Computing and Flow Analysis on Discretionary Access Control: Solving the Propagation Problem
"... Abstract. Based on granular computing, information flows in Discretionary Access Control (DAC) are examined. DAC are classified in the following nested order: From general to specific, binary neighborhood systems(binary relations),topological spaces (reflexive and transitive relations) and clopen sp ..."
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Abstract. Based on granular computing, information flows in Discretionary Access Control (DAC) are examined. DAC are classified in the following nested order: From general to specific, binary neighborhood systems(binary relations),topological spaces (reflexive and transitive relations) and clopen spaces (equivalence relations) in geometric (algebraic) terms. In security terms, the two smaller classes meet information flow security and Chinese wall security policy in respective order. Roughly, information flow security policy (IFSP) means any data can never flow or propagate into the enemy hands of the initial owner. Chinese wall security policy is IFSP, in which enemy is a symmetric relation. Keywords—component, formatting, style, styling, insert (key words) I.
Toward a Theory of Granular Computing
"... Abstract — A theory of granular computing is presented. Some classical example are examined to obtain the hint of new directions Obvious examples are dynamic programming, fuzzy numbers, infinitesimal number and access control model, (pre)topological spaces are examined; Localized multilevel granul ..."
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Abstract — A theory of granular computing is presented. Some classical example are examined to obtain the hint of new directions Obvious examples are dynamic programming, fuzzy numbers, infinitesimal number and access control model, (pre)topological spaces are examined; Localized multilevel granulation can be modeled by generalized topological spaces, called neighborhood systems. For most general granulation are modeled by Tarski type relational structures. Index Terms — binary relation, granular computing, neighborhood system, topology. I.
Granular Computing: Examples, Intuitions and Modeling
"... Abstract — The notion of granular computing is examined. Obvious examples, such as fuzzy numbers, infinitesimal number and access control model, (pre)topological spaces are examined. A general models are proposed; Localized multilevel granulation can be modeled by generalized topological spaces, c ..."
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Abstract — The notion of granular computing is examined. Obvious examples, such as fuzzy numbers, infinitesimal number and access control model, (pre)topological spaces are examined. A general models are proposed; Localized multilevel granulation can be modeled by generalized topological spaces, called neighborhood systems. For most general granulation are modeled by Tarski type relational structures. Index Terms — binary relation, granular computing, neighborhood system, topology. I.