Mining Web functional dependencies for flexible information access

Perugini, Saverio; Ramakrishnan, Naren

doi:10.1002/asi.20628

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Other studies presented in [3,7,9,11,12] have presented various methods for discovering FDs from large databases, these studies have focused on discovering FDs from very large databases and had faced a general problem which is represented by the exponential time requirements that depend on database size (the dimensionality problem in number of tuples and attributes). …”

Section: Inroductionmentioning

confidence: 99%

“…We will compare the result of our proposed algorithm with a previous well known algorithm called FD_MINE [12] . Some of the previous studies in discovering FDs from databases presented in [2,4,8] have focused on discovering embedded parallel query execution, optimizing queries, providing some kind of summaries over large data sets or discovering association rules in stream data.Other studies presented in [3,7,9,11,12] have presented various methods for discovering FDs from large databases, these studies have focused on discovering FDs from very large databases and had faced a general problem which is represented by the exponential time requirements that depend on database size (the dimensionality problem in number of tuples and attributes). …”

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Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover

Atoum¹,

Bader²,

Awajan³

2008

J. of Computer Science

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Data Mining (DM) represents the process of extracting interesting and previously unknown knowledge from data. This study proposes a new algorithm called FD_Discover for discovering Functional Dependencies (FDs) from databases. This algorithm employs some concepts from relational databases design theory specifically the concepts of equivalences and the minimal cover. It has resulted in large improvement in performance in comparison with a recent and similar algorithm called FD_MINE. Key words:Data mining, functional dependencies, equivalent classes, minimal cover INRODUCTIONFDs are relationships (constraints) between the attributes of a database relation; a FD states that the value of some attributes are uniquely determined by the values of some other attributes. Discovering FDs from databases is useful for reverse engineering of legacy systems for which the design information has been lost. Furthermore, discovering FDs can also help a database designer to decompose a relational schema into several relations through the normalization process to get rid or eliminate some of the problems of unsatisfactory database design. The identification of these dependencies that are satisfied by a database instance is an important topic in data mining literature [5] . The problem of generating or discovering FDs from relational databases had been studied in [3,4,7,8,11,12] . A straight forward solution algorithm is shown to require exponential time of all inputs (number of attributes and number of tuples in a relation).In this study, we are proposing a new algorithm for discovering FDs from static databases. This algorithm will use and employ some concepts from relational database theory, such as the theory of equivalencies and minimal cover of FDs. The proposed algorithm aims at minimizing the time requirements of algorithms that discover FDs from databases. We will compare the result of our proposed algorithm with a previous well known algorithm called FD_MINE [12] . Some of the previous studies in discovering FDs from databases presented in [2,4,8] have focused on discovering embedded parallel query execution, optimizing queries, providing some kind of summaries over large data sets or discovering association rules in stream data.Other studies presented in [3,7,9,11,12] have presented various methods for discovering FDs from large databases, these studies have focused on discovering FDs from very large databases and had faced a general problem which is represented by the exponential time requirements that depend on database size (the dimensionality problem in number of tuples and attributes). Definition 1:If A is an attribute or set of attributes in relation R, all the attributes in R that are functionally dependent on A in relation R with respect to F (where F is a set of FDs that holds on R), form the closure of A and it is denoted by A + or Closure(A).Definition 2: The nontrivial closure of attribute A with respect to F that is denoted by Closure→(A), is defined as Closure→(A) = Closure (A)-A.

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Section: Inroductionmentioning

confidence: 99%

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confidence: 99%

Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover

Atoum¹,

Bader²,

Awajan³

2008

J. of Computer Science

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Program transformations for information personalization

Perugini

Ramakrishnan

2010

Computer Languages, Systems & Structures

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Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation. The central thesis of this dissertation is that modeling interaction explicitly in a representation, and studying how partial information can be harnessed in it by program transformations to direct the flow of the interaction, can provide insight into, reveal opportunities for, and define a model for personalized interaction. To evaluate this thesis, a formal modeling methodology is developed for personalizing interactions with information systems, especially hierarchical hypermedia, based on program transformations. The predominant form of personalized interaction developed in this thesis is out-of-turn interaction, a technique which empowers the user to take the initiative in a user-system dialog by providing unsolicited, but relevant, information out-of-turn. Out-of-turn interaction helps flexibly bridge any mismatch between the user's model of information seeking and the system's hardwired hyperlink structure in a manner fundamentally different from extant solutions, such as multiple faceted browsing classifications and search tools. This capability is showcased through two interaction interfaces using alternate modalities to capture and communicate out-of-turn information to the underlying system: a toolbar embedded into a traditional browser for out-of-turn textual input and voice-enabled content pages for out-of-turn speech input. The specific research issues addressed involve identifying and developing representations and transformations suitable for general classes of hierarchical hypermedia, providing supplemental interactions for improving the personalized experience, and studying user's (out-of-turn) interactions with resulting systems. ' 'You can't run with the hares and hunt with the hounds." Anonymous, but popularized by N. Ramakrishnan I pay all homage to Jesus Christ, my Lord, Savior, and Redeemer, through whom all things are possible. Pursuing a Ph.D. is like a boxing match. During the course of the match, the boxer is expected to take some really good shots (the agony of the quals, the sleepless nights spent preparing a proposal), but persevere, and prevail victoriously (bringing structure to amorphous research issues). This match has been a labor of love, the best experience of my life, one which no money or fancy company stock options could buy, one which I would trade for no other. As the popular advertising slogan goes: 'completing an undergraduate degree: expected; aspiring to get a Master's: ambitious; pursuing a Ph.D.: priceless.' Moreover, there is a certain intangible and irresistible camaraderie and solidarity among Ph.D. students which permeates and enriches the soul. Few things in life are as beautiful. Thus, special thanks are due to several instrumental people for making this process possible and so enjoyable. Chief among them is my thesis advisor, Naren Ramakrishnan...

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Supporting multiple paths to objects in information hierarchies: Faceted classification, faceted search, and symbolic links

Perugini

2010

Information Processing & Management

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Mining Web functional dependencies for flexible information access

Cited by 3 publications

References 23 publications

Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover

Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal Cover

Program transformations for information personalization

Supporting multiple paths to objects in information hierarchies: Faceted classification, faceted search, and symbolic links

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