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Towards Connecting database applications to Ontologies

Towards Connecting Database Applications to Ontologies

引用

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

作者： Chimène Fankam Stéphane Jean Guy Pierra Ladjel Bellatreche Yamine Aït Ameur LISI/ENSMA Poitiers University France LISI/ENSMA Poitiers University Futuroscope France Universite de Poitiers Poitiers Poitou-Charentes FR

Most database applications are designed according the ANSI/SPARC architecture. When it is used a large amount of semantics of data may be lost during the transformation from the conceptual model to a logical model. As a consequence exchanging/integrating various databases or generating user interfaces for data access become difficult. Ontologies seem an interesting solution to solve these problems, since they allow making explicit the semantics of data. In this paper, we propose an ontology-based approach for designing database applications, and then, for representing explicitly the semantics of data within the database. It consists in extending the ANSI/SPARC architecture with the ontological level. Note that this extension may also be added to existing applications designed according to the ANSI/SPARC architecture, since it preserves an upward compatibility.

关键词： Joining processes Ontologies data models Process design Relational databases User interfaces Memory database systems Design methodology data mining

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Replay the Execution History of Rule-Based Information

Replay the Execution History of Rule-Based Information

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

作者： Essam Mansour Hagen Höpfner School of Information Technology International University Bremen Bruchsal Germany

Software systems in health care, such as disease and medical-record management, or financial applications, such as customer relationship and portfolio management, have very often a temporal nature. Information is specified in form of rules as a pre-step for monitoring the changes of interest in the application. Managing such applications requires to provide replay support for managed information at any specified review period. This paper presents a replay support method for information formalized as rules. This method is based on an XML-based model and a replay query language for the rule-based information. We introduce the model and the language using a clinical case study, and evaluates the storage efficiency of the model and the performance of the query.

关键词： History Application software Customer relationship management Financial management Software systems Medical services Diseases Portfolios Monitoring Information management

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9th international conference on Practical applications of Computational Biology and Bioinformatics 1

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丛书名： advances in Intelligent Systems and Computing 375

2015年

作者： Ross Overbeek, Miguel P. Rocha, Florentino Fdez-Riverola, Juan F. De Paz (eds.)

ISBN: (数字)9783319197753;9783319197760

ISBN: (纸本)9783319197753;9783319197760

This proceedings presents recent practical applications of Computational Biology and Bioinformatics. It contains the proceedings of the 9th international conference on Practical applications of Computational Biology & Bioinformatics held at University of Salamanca, Spain, at June 3rd-5th, 2015. The international conference on Practical applications of Computational Biology & Bioinformatics (PACBB) is an annual international meeting dedicated to emerging and challenging applied research in Bioinformatics and Computational Biology. Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next generation sequencing technologies, together with novel and ever evolving distinct types of omics data technologies, have put an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as databases, Statistics, data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information requiring tools from the computational sciences.

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Visualization and Integration of databases Using Self-Organizing Map

Visualization and Integration of Databases Using Self-Organi...

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

作者： Farid Bourennani Ken Q. Pu Ying Zhu Institute of Technology University of Ontario Canada

With the growing computer networks, accessible data is becoming increasingly distributed. Understanding and integrating remote and unfamiliar data sources are important data management issues. In this paper, we propose to utilize self-organizing maps (SOM) clustering to aid with the visualization of similar columns, and integration of relational database tables and attributes based on the content. In order to accommodate heterogeneous data types found in relational databases, we extended the TFIDF measure to handle, in addition to text, numerical attribute types for coincident meaning extraction. We present a SOM clustering based visualization algorithm allowing the user to browse the heterogeneously typed database attributes and discover semantically similar clusters. Additionally, we propose a new algorithm Common Item Based Classifier (CIBC) to smoothen the homogeneity of the clusters obtained by SOM. The discovered semantic clusters can significantly aid in manual or automated constructions of data integrity constraints in data cleaning or schema mappings in data integration.

关键词： Visual databases data visualization Distributed databases Clustering algorithms Relational databases Self organizing feature maps data mining Information retrieval Application software Computer network management

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Archiving with Athamas: A Framework for Optimized Handling of Domain knowledge

Archiving with Athamas: A Framework for Optimized Handling o...

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

作者： Eric R. Schendel Ahmed M. Mahdy Department of Computing Sciences Texas A and M University Corpus Christi Corpus Christi TX USA

ISBN: (纸本)9781424460816

Continuous changes in requirements of a multi-tiered project can significantly deteriorate the maintenance process. This is mainly due to incompatibility between application design and newly introduced requirements. This paper presents a potential application framework, Athamas, which provides a scalable way to agilely adapt to changing data requirements. A currently functional initiative of Athamas is to allow domain knowledge generation from application components without users or business processes dictating data handling and integration requirements. This development allows the framework to be used (1) today as a use case alternative to relational databases for archiving domain knowledge into storage containers and (2) in the future for optimally extracting the knowledge from the storage containers. Evaluations of Athamas-based applications are made against applications using MySQL's MyISAM and ARCHIVE database storage engines for data archival purposes. Athamas using a zlib compression layer significantly reduces the storage size utilization to 3% of MyISAM and 24% of ARCHIVE, along with publishing time improvements by a factor of 4.9 and 2.6 respectively. Using Athamas with bzip2 compression, storage usage is down to 8% of ARCHIVE usage but at a cost of slower performance up to 4.5 times. The paper also discusses future enhancements to Athamas allowing users to efficiently query domain knowledge.

关键词： Application software Relational databases Containers data mining Oceans Engines data handling Publishing Costs Content management

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Discovering Informative Syntactic Relationships between Named Entities in Biomedical Literature

Discovering Informative Syntactic Relationships between Name...

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

作者： Annalisa Appice Michelangelo Ceci Corrado Loglisci Dipartimento di Informatica Università degli Studi di Bari Aldo Moro Bari Italy

ISBN: (纸本)9781424460816

The discovery of new and potentially meaningful relationships between named entities in biomedical literature can take great advantage from the application of multirelational data mining approaches in text mining. This is motivated by the peculiarity of multi-relational data mining to be able to express and manipulate relationships between entities. We investigate the application of such an approach to address the task of identifying informative syntactic structures, which are frequent in biomedical abstract corpora. Initially, named entities are annotated in text corpora according to some biomedical dictionary (e.g. MeSH taxonomy). Tagged entities are then integrated in syntactic structures with the role of subject and/or object of the corresponding verb. These structures are represented in a first-order language. Multi-relational approach to frequent pattern discovery allows to identify the verb-based relationships between the named entities which frequently occur in the corpora. Preliminary experiments with a collection of abstracts obtained by querying Medline on a specific disease are reported.

关键词： data mining Text mining Speech Search methods Proteins databases Dictionaries Taxonomy Abstracts Diseases

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引用

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

作者： Martin Suntinger Hannes Obweger Josef Schiefer Philip Limbeck Günther Raidl UC4 Senactive Software GmbH Vienna Austria Institute of Computer Graphics and Algorithms University of Technology Vienna Vienna Austria

ISBN: (纸本)9781424460816

In this paper, we present a novel approach towards time-series similarity search. Our technique relies on trends in a curve's movement over time. A trend is characterized by a series', values channeling in a certain direction (up, down, sideways) over a given time period before changing direction. We extract trend-turning points and utilize them for computing the similarity of two series based on the slopes between their turning points. For the turning point extraction, well-known techniques from financial market analysis are applied. The method supports queries of variable lengths and is resistant to different scaling of query and candidate sequence. It supports both subsequence searching and full sequence matching. One particular focus of this work is to enable simple modeling of query patterns as well as efficient similarity score updates in case of appending new data points.

关键词： Turning Electronic mail Pattern matching databases Application software Computer graphics Software algorithms Immune system data analysis Time series analysis

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From Tables to knowledge: Recent advances in Table Understanding 21

From Tables to Knowledge: Recent Advances in Table Understan...

引用

27th ACM SIGKDD international conference on knowledge Discovery and data Mining (KDD)

作者： Pujara, Jay Szekely, Pedro Sun, Huan Chen, Muhao USC Informat Sci Inst Marina Del Rey CA 90292 USA Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781450383325

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and surface forms used to express tables. Recent advances in representation learning and knowledge representation have made progress in exploiting structural regularities in tabular data to unlock this knowledge. In this tutorial, we provide a survey of these advances for a host of table understanding tasks, including table segmentation, semantic typing of cells, transforming tables to knowledge graphs, entity linking, and table retrieval tasks for question answering. The structure of the tutorial will include three major modules. The first will provide attendees an introduction to the seminal work in organization of data in tables, and cover the major goals and approaches of computational systems that undertake table understanding. The second module will cover specific models used for table understanding tasks, such as table discovery, table segmentation and layout detection, cell classification and semantic typing, mapping tables to knowledge graphs and linking to known entities, and table retrieval in search and question answering. The final tutorial module will provide a primer for researchers who want to get involved with the table understanding community, providing them a guide to the most commonly used benchmark datasets and models, downstream applications and evaluations, and a sketch of the open problems in table understanding. https://***/KDD21Tutorial/

关键词： tabular data representation learning knowledge graphs semantic typing entity linking probabilistic models

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Big data, knowledge Organization and decision making: opportunities and limits 15

Big data, Knowledge Organization and decision making: opport...

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15th international conference international Society for knowledge Organization, ISKO 2018

作者： David, Amos Ndjock, Nadine

ISBN: (纸本)9783956504204

A concept that is currently attracting much interest in the field of information science is the concept of big data. This paper tries to answer some of the questions raised by big data such as how it relates to knowledge organization and decision making, and what its opportunities and limits are. Big data is defined as a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process low-latency data. It also has one or more of the following characteristics: high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, the web, and social media - much of it generated in real time and on a very large scale1 © 2018 international Society for knowledge Organization. All rights reserved.

关键词： Big data

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Distributed Key Management in Dynamic Outsourced databases: A Trie-Based Approach

Distributed Key Management in Dynamic Outsourced Databases: ...

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

作者： V. El-khoury N. Bennani A. M. Ouksel CNRS INSA-Lyon Lyon University France CNRS Lyon Univ. Lyon Dept. of Information and Decision Sciences The University of Illinois Chicago IL USA

The decision to outsource databases is strategic in many organizations due to the increasing costs of internally managing large volumes of information. The sensitive nature of this information raises the need for powerful mechanisms to protect it against unauthorized disclosure. Centralized encryption to access control at the data owner level has been proposed as one way of handling this issue. However, its prohibitive costs renders it impractical and inflexible. A distributed cryptographic approach has been suggested as a promising alternative, where keys are distributed to users on the basis of their assigned privileges. But in this case, key management becomes problematic in the face of frequent database updates and remains an open issue. In this paper, we present a novel approach based on binary tries. By exploiting the intrinsic properties of these data structures, key management complexity, and thus its cost, is significantly reduced. Changes to the binary trie structure remain limited in the face of frequent updates. Preliminary experimental analysis demonstrates the validity and the effectiveness of our approach.

关键词： Distributed databases Cryptography Access control Costs conference management knowledge management Protection Outsourcing data structures Innovation management

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