The project concerns on the enhancement of the territory, from architecture to energy/infrastructures. Part of the project includes the involvement of urban people, according to there interpretation of the concept of social and work-life cycles of citizenship, energy and its saving, info on city (safety and comfort). This is the era of the standards of quality of life and productivity, with specific indicators that define the concept of Smart City through the social assets, economy, environment, governance, mobility and housing.
{"title":"Human Smarties: The Human Communities of the Future","authors":"Maria Chiara Fagioli","doi":"10.1109/ICCSA.2015.14","DOIUrl":"https://doi.org/10.1109/ICCSA.2015.14","url":null,"abstract":"The project concerns on the enhancement of the territory, from architecture to energy/infrastructures. Part of the project includes the involvement of urban people, according to there interpretation of the concept of social and work-life cycles of citizenship, energy and its saving, info on city (safety and comfort). This is the era of the standards of quality of life and productivity, with specific indicators that define the concept of Smart City through the social assets, economy, environment, governance, mobility and housing.","PeriodicalId":197153,"journal":{"name":"2015 15th International Conference on Computational Science and Its Applications","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117200867","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}
The enduring revolution in the field of life sciences is producing biological data at phenomenal rate. The large volume of biological data is meaningful only when it is accessed and analyzed by different researchers. A flood of biological databases is creating a problem in finding an efficient query execution plan for the complex query as posed by bio-researchers. In this research paper, an effort has been made to effectively process the biological data collected in biobanks. The major objective of this research work is to find an optimal query execution plan for biobank database queries using the proposed Restricted Exhaustive Enumeration Approach (REA) and Entropy based Restricted Genetic Approach (ERGA). The result of different query optimization approaches viz. Exhaustive Enumeration, Restricted Exhaustive Enumeration, Simple Genetic Approach and Entropy Based Restricted Genetic Approach are compared with each other on the basis of usage of system resources. The results of Entropy Based Restricted Genetic Algorithm in finding query execution plan for Burbank queries are better than EA and SGAby 2-20% and 7-15% respectively. Furthermore, experimental results reveal that use of Inter-site parallel environment further optimized the results of ERGA by 0-4%.
{"title":"Design and Analysis of Stochastic Query Optimizer for Biobank Databases","authors":"Manik Sharma, Gurvinder Singh, Rajinder Singh, Jasbir Singh","doi":"10.1109/ICCSA.2015.17","DOIUrl":"https://doi.org/10.1109/ICCSA.2015.17","url":null,"abstract":"The enduring revolution in the field of life sciences is producing biological data at phenomenal rate. The large volume of biological data is meaningful only when it is accessed and analyzed by different researchers. A flood of biological databases is creating a problem in finding an efficient query execution plan for the complex query as posed by bio-researchers. In this research paper, an effort has been made to effectively process the biological data collected in biobanks. The major objective of this research work is to find an optimal query execution plan for biobank database queries using the proposed Restricted Exhaustive Enumeration Approach (REA) and Entropy based Restricted Genetic Approach (ERGA). The result of different query optimization approaches viz. Exhaustive Enumeration, Restricted Exhaustive Enumeration, Simple Genetic Approach and Entropy Based Restricted Genetic Approach are compared with each other on the basis of usage of system resources. The results of Entropy Based Restricted Genetic Algorithm in finding query execution plan for Burbank queries are better than EA and SGAby 2-20% and 7-15% respectively. Furthermore, experimental results reveal that use of Inter-site parallel environment further optimized the results of ERGA by 0-4%.","PeriodicalId":197153,"journal":{"name":"2015 15th International Conference on Computational Science and Its Applications","volume":"29 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126210418","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}
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