{"title":"An Extension of XQuery for Graph Analysis of Biological Pathways","authors":"L. Strömbäck, S. Schmidt","doi":"10.1109/DBKDA.2009.16","DOIUrl":"https://doi.org/10.1109/DBKDA.2009.16","url":null,"abstract":"The vast quantity of scientific data produced in life sciences demands the use of sophisticated storage and analysis techniques. In particular, for biological pathways graph analysis plays an important role and data is commonly available in XML-based formats. Thus, there is a growing need to make analysis capabilities available through query languages for XML. This paper presents an approach to extend XQuery for graph analysis with focus on data for biological pathways. A graph model is introduced within the XQuery environment. New built-in functions define the available operations on the graph model. XQuery expressions can be utilized to populate graphs with data and execute graph algorithms. Graph data and results of algorithms can be accessed in an XML representation for further processing. In addition, a reference mechanism can be used to preserve associations from graph data to the original XML data. The approach has been implemented as an extension to exist. First evaluations of the implementation show that the introduced approach is practical and efficient for reaction networks with several thousand vertices and edges.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116798797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-level Topological Relations of the Spatial Reasoning System RCC-8","authors":"A. Alboody, F. Sèdes, J. Inglada","doi":"10.1109/DBKDA.2009.13","DOIUrl":"https://doi.org/10.1109/DBKDA.2009.13","url":null,"abstract":"Queries in geospatial databases, such as Geographic Information Systems (GIS), image databases, are often based upon the relations among spatial objects. Spatial relations are the basis of many queries that GIS perform such as the topological relations. The general description of region-region topological relations in details is still an unsolved issue although much effort has been done.The eight basic topological relations between two spatial regions are written without any details in the classical form of the spatial reasoning system RCC-8: DC, EC, EQ, PO, TPP, TPPi, NTPP and NTPPi. In some applications, there are needs to describe in details these relations. In order to extract all the necessary details at all levels and to differentiate between relations of the same kind, multi-level topological relations of RCC-8 are introduced by using two concepts: the Separation Number and the Types of Spatial Elements (Points and Lines) of the Boundary-Boundary Intersection Spatial Set (BBISS). These two concepts are very important to detail these relations.In our study, the major contribution is multi-level topological relations of RCC-8. We focus our work on the four relations EC, PO, TPP and TPPi which can be detailed at two additional levels. First, these four relations are written and described in general detailed forms by the concept of Separation Number. Secondly, the same relations are expressed in other general forms by the concept of Types of Spatial Elements. Finally, examples are provided to illustrate the determination of these relations presented in this paper.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115480399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualization and Integration of Databases Using Self-Organizing Map","authors":"F. Bourennani, K. Pu, Ying Zhu","doi":"10.1109/DBKDA.2009.30","DOIUrl":"https://doi.org/10.1109/DBKDA.2009.30","url":null,"abstract":"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.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Web Based Medical Expert System with a Self Training Heuristic Rule Induction Algorithm","authors":"I. Chorbev, D. Mihajlov, I. Jolevski","doi":"10.1109/DBKDA.2009.21","DOIUrl":"https://doi.org/10.1109/DBKDA.2009.21","url":null,"abstract":"This paper presents a web based medical expert system that performs self training using a heuristic rule induction algorithm. The data inserted by medical personnel while using the expert system is subsequently used for additional learning. The system is trained using a hybrid heuristic algorithm for induction of classification rules that we previously developed. The SA Tabu Miner algorithm (Simulated Annealing and Tabu Search based Data Miner) is inspired by both research on heuristic optimization algorithms and rule induction data mining concepts and principles. In this paper we compare the performance of SA Tabu Miner with other rule induction algorithms for classification on public domain data sets.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on Cost-Sensitive Communication Models over Distributed Data Streams Processing","authors":"Aiping Li, Li Tian, Yan Jia, Shuqiang Yang","doi":"10.1109/DBKDA.2009.19","DOIUrl":"https://doi.org/10.1109/DBKDA.2009.19","url":null,"abstract":"Large-scaled distributed monitoring systems are in face of the challenge of massive data and resource restriction. Prediction models can be used to reduce communication cost over the net. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still guaranteeing sufficient precision of query results. Prediction models are also proposed to process prediction queries over future data streams in this paper. Three particular models, static model, linear model and acceleration model, and the corresponding tuning schemas are given. Experimentations are performed based on the simulated data and ocean air temperature data measured by TAO (tropical atmosphere ocean). Analytical and experimental evidence show that the proposed approach significantly reduces overall communication cost and performs well over prediction queries.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123545095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}