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Source-Aware Repairs for Inconsistent databases

Source-Aware Repairs for Inconsistent Databases

引用

international conference on advances in databases, knowledge, and data applications, dbkda

作者： Navin Viswanath Rajshekhar Sunderraman Department of Computer Science Georgia State University Atlanta GA USA

The problem of extracting consistent query answers from an inconsistent database has been mainly approached from two directions: ldquorepairingrdquo the database or rewriting queries so that only consistent answers are returned. Logic programming with explicit negation has been widely used in order to specify repairs such that each answer set of the repair program corresponds to a repair. In this paper, we explore the problem of obtaining so called ldquopreferred repairsrdquo from a database that is both inconsistent and incomplete based on preferences for the source from which the information is obtained. We show how a preferred repair might be specified using logic programs when source information is available.

关键词： Computer science Logic programming Relational databases Application software data mining data models

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An Application of data Mining to Improve Personnel Performance Evaluation in Higher Education Sector In India 2

An Application of Data Mining to Improve Personnel Performan...

引用

international conference on advances in Computer Engineering and applications (ICACEA)

作者： Sharma, Mugdha Goyal, Ankit Rukmini Devi Inst Adv Studies Delhi India FORE Sch Management Delhi India

ISBN: (纸本)9781467369114

data mining is a process of extracting knowledge from large databases. knowledge is appreciated as ultimate power now a days and considered as very important factor for the success of any organization because it has impacted the role of people working in that organization. In last decade, the higher education sector in India has grown radically. Thus HR management has implemented various improvements and it is hard for organizations to retain a motivated and competent workforce. Clever investment in human resource and its effective management has become very necessary today. HRM in higher education sector is further complicated by qualitative and multidimensional definition of "performance" of an academician (employee) in this sector. This paper aims to propose a model for predicting employee performance in higher education institutes with the help of classification and prediction techniques of data mining. The objective of model is to facilitate decisions in a challenging and emerging field of HRM in higher education sector.

关键词： Classification & Prediction Technique Employee Performance data Mining Higher Education HRM

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advances in Computational Intelligence, Part III 2012

引用

丛书名： Communications in Computer and Information Science

2012年

作者： Salvatore Greco Bernadette Bouchon-Meunier Giulianella Coletti Mario Fedrizzi Benedetto Matarazzo Ronald R. Yager

ISBN: (数字)9783642317187

ISBN: (纸本)9783642317170

These four volumes (CCIS 297, 298, 299, 300) constitute the proceedings of the 14th international conference on Information Processing and Management of Uncertainty in knowledge-Based Systems, IPMU 2012, held in Catania, Italy, in July 2012. The 258 revised full papers presented together with six invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy machine learning and on-line modeling; computing with words and decision making; soft computing in computer vision; rough sets and complex data analysis: theory and applications; intelligent databases and information system; information fusion systems; philosophical and methodological aspects of soft computing; basic issues in rough sets; 40th anniversary of the measures of fuziness; SPS11 uncertainty in profiling systems and applications; handling uncertainty with copulas; formal methods to deal with uncertainty of many-valued events; linguistic summarization and description of data; fuzzy implications: theory and applications; sensing and data mining for teaching and learning; theory and applications of intuitionistic fuzzy sets; approximate aspects of data mining and database analytics; fuzzy numbers and their applications; information processing and management of uncertainty in knowledge-based systems; aggregation functions; imprecise probabilities; probabilistic graphical models with imprecision: theory and applications; belief function theory: basics and/or applications; fuzzy uncertainty in economics and business; new trends in De Finetti's approach; fuzzy measures and integrals; multicriteria decision making; uncertainty in privacy and security; uncertainty in the spirit of Pietro Benvenuti; coopetition; game theory; probabilistic approach.

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advances in Computational Intelligence, Part IV 1st ed. 2012

引用

丛书名： Communications in Computer and Information Science

2012年

作者： Salvatore Greco Bernadette Bouchon-Meunier Giulianella Coletti Mario Fedrizzi Benedetto Matarazzo Ronald R. Yager

ISBN: (数字)9783642317248

ISBN: (纸本)9783642317231

These four volumes (CCIS 297, 298, 299, 300) constitute the proceedings of the 14th international conference on Information Processing and Management of Uncertainty in knowledge-Based Systems, IPMU 2012, held in Catania, Italy, in July 2012. The 258 revised full papers presented together with six invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy machine learning and on-line modeling; computing with words and decision making; soft computing in computer vision; rough sets and complex data analysis: theory and applications; intelligent databases and information system; information fusion systems; philosophical and methodological aspects of soft computing; basic issues in rough sets; 40th anniversary of the measures of fuziness; SPS11 uncertainty in profiling systems and applications; handling uncertainty with copulas; formal methods to deal with uncertainty of many-valued events; linguistic summarization and description of data; fuzzy implications: theory and applications; sensing and data mining for teaching and learning; theory and applications of intuitionistic fuzzy sets; approximate aspects of data mining and database analytics; fuzzy numbers and their applications; information processing and management of uncertainty in knowledge-based systems; aggregation functions; imprecise probabilities; probabilistic graphical models with imprecision: theory and applications; belief function theory: basics and/or applications; fuzzy uncertainty in economics and business; new trends in De Finetti's approach; fuzzy measures and integrals; multi criteria decision making; uncertainty in privacy and security; uncertainty in the spirit of Pietro Benvenuti; coopetition; game theory; probabilistic approach.

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Clustering Relational database Entities Using K-means

Clustering Relational Database Entities Using K-means

引用

international conference on advances in databases, knowledge, and data applications, dbkda

作者： Farid Bourennani Mouhcine Guennoun Ying Zhu Institute of Technology University of Ontario ONT Canada

The fast evolution of hardware and the internet made large volumes of data more accessible. This data is composed of heterogeneous data types such as text, numbers, multimedia, and others. Non-overlapping research communities work on processing homogeneous data types. Nevertheless, from the user perspective, these heterogeneous data types should behave and be accessed in a similar fashion. Processing heterogeneous data types, which is Heterogeneous data Mining (HDM), is a complex task. However, the HDM by Unified Vectorization (HDM-UV) seems to be an appropriate solution for this problem because it permits to process the heterogeneous data types simultaneously. In this paper, we use K-means and Self-Organizing Maps for simultaneously processing textual and numerical data types by UV. We evaluate how the HDM-UV improves the clustering results of these two algorithms (SOM, K-means) by comparing them to the traditional homogeneous data processing. Furthermore, we compare the clustering results of the two algorithms applied to a data integration problem.

关键词： Relational databases data mining Clustering algorithms Weight measurement Business Clustering methods Biomedical measurements Mining industry Companies Hardware

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Mining Regular High Utility Sequential Patterns in Static and Dynamic databases 13th

Mining Regular High Utility Sequential Patterns in Static an...

引用

13th international conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ishita, Sabrina Zaman Ahmed, Chowdhury Farhan Leung, Carson K. Hoi, Calvin H. S. Univ Dhaka Dhaka Bangladesh Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9783030190637;9783030190620

Regular pattern mining has been emerged as one of the important sub-domains of data mining with its numerous applications. Although patterns that occur at a regular interval throughout the whole database can lead to interesting knowledge, examining the utility values of these patterns can unveil more interesting useful information. In a sequence database, the task of mining regular high utility patterns can be more challenging. In this paper, we first propose a new algorithm for mining regular high utility sequential patterns from static databases. As handling of the incremental nature of big data brings useful results in many applications in the recent era of big data, we then extend our algorithm to mine regular high utility sequential patterns from dynamic databases. Evaluation results on several real-life datasets show the effectiveness of our two algorithms.

关键词： data mining Regular pattern mining High utility sequential pattern Incremental mining Information management Information processing management

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Transformers for Tabular data Representation: A Tutorial on Models and applications 48th

Transformers for Tabular Data Representation: A Tutorial on ...

引用

48th international conference on Very Large databases (VLDB)

作者： Badaro, Gilbert Papotti, Paolo EURECOM Biot France

In the last few years, the natural language processing community witnessed advances in neural representations of free texts with transformer-based language models (LMs). Given the importance of knowledge available in relational tables, recent research efforts extend LMs by developing neural representations for tabular data. In this tutorial, we present these proposals with two main goals. First, we introduce to a database audience the potentials and the limitations of current models. Second, we demonstrate the large variety of data applications that benefit from the transformer architecture. The tutorial aims at encouraging database researchers to engage and contribute to this new direction, and at empowering practitioners with a new set of tools for applications involving text and tabular data.

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An Adaptive Synchronization Policy for Harvesting OAI-PMH Repositories

An Adaptive Synchronization Policy for Harvesting OAI-PMH Re...

引用

international conference on advances in databases, knowledge, and data applications, dbkda

作者： Noha Adly Department of Computer and Systems Engineering Faculty of Engineering Alexandria University Alexandria Egypt

Metadata harvesting requires timely propagation of up-to-date information from thousands of repositories over a wide area network. It is desirable to keep the data as fresh as possible while observing the overhead on the harvester. An important dimension to be considered is that repositories vary widely in their update patterns; they may experience different update rates at different times or unexpected changes to update patterns. In this paper, we define data freshness metrics and propose an adaptive algorithm for the synchronization of the harvester with the repositories. The algorithm is based on meeting a desired level of freshness while incurring the minimum overhead on the harvester. We present a comparison between different policies for the synchronization within the framework devised. It is shown that the proposed policy outperform the other policies, especially for heterogeneous update patterns.

关键词： Search engines data engineering knowledge engineering Software libraries Streaming media Feeds databases Application software Computer networks Systems engineering and theory

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Indexing Semi-structured data for Efficient Handling of Branching Path Expressions

Indexing Semi-structured Data for Efficient Handling of Bran...

引用

international conference on advances in databases, knowledge, and data applications, dbkda

作者： Talles Brito Thiago Ribeiro Glêdson Elias Compose-Component Oriented Service Engineering Group Federal University of Paraiba Joao Pessoa Brazil

ISBN: (纸本)9781424460816

One of the major challenges in development of indexing techniques for semi-structured data is related to how to index the data structural properties. The main issue is how to efficiently handle branching path expressions without suffering from undesired growth of query processing costs and index file sizes. Several proposals for indexing semi-structured data can be found in the literature. However, in order to reduce index file sizes, most of them do not index or handle branching path expressions. Considering those ones that do that, they usually suffer from high query processing costs and large index file sizes. In such a context, this paper proposes a path-based indexing technique for semi-structured data, which deals with a well-defined class of branching path expressions. As evinced by experimental evaluation, the adoption of the proposed technique results in excellent query processing time and generates index file sizes close to data input file sizes.

关键词： Indexing Query processing XML Costs Proposals Navigation databases data engineering knowledge engineering Informatics

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Querying Trajectory data Warehouses

Querying Trajectory Data Warehouses

引用

international conference on advances in databases, knowledge, and data applications, dbkda

作者： Hoda M. O. Mokhtar Gihan Mahmoud Information Systems Department Faculty of Computers and Information Cairo University Egypt DataGear BI Research Egypt

With the fast development in mobile and communication technology, the need for moving object databases increases. Moving object databases constitute a major ingredient in many applications that are location sensitive. Traffic control, fleet management, m-commerce, and E-911 are among those emerging location based services (LBSs). Along with the vast increase of location information, and the need to efficiently manage and mine those information, spatio-temporal data warehouses arise into the picture requiring new algorithms, new measures, and efficient querying techniques to efficiently utilize the historical information obtained from moving objects. In this paper we present a spatio-temporal data warehouse (STDW) that enables querying location information. Experimental results on our query performance are presented.

关键词： data warehouses databases Application software Decision making Multidimensional systems Bluetooth Information systems Bismuth Mobile computing Communications technology

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