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Fast Structural Search in Phylogenetic Databases
Jason T. L. Wang,Huiyuan Shan,Dennis Shasha,William H. Piel
Evolutionary Bioinformatics , 2005,
Abstract: As the size of phylogenetic databases grows, the need for efficiently searching these databases arises. Thanks to previous and ongoing research, searching by attribute value and by text has become commonplace in these databases. However, searching by topological or physical structure, especially for large databases and especially for approximate matches, is still an art. We propose structural search techniques that, given a query or pattern tree P and a database of phylogenies D, find trees in D that are sufficiently close to P . The “closeness” is a measure of the topological relationships in P that are found to be the same or similar in a tree D in D. We develop a filtering technique that accelerates searches and present algorithms for rooted and unrooted trees where the trees can be weighted or unweighted. Experimental results on comparing the similarity measure with existing tree metrics and on evaluating the efficiency of the search techniques demonstrate that the proposed approach is promising
FDB: A Query Engine for Factorised Relational Databases  [PDF]
Nurzhan Bakibayev,Dan Olteanu,Jakub Závodny
Computer Science , 2012,
Abstract: Factorised databases are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. This paper introduces FDB, an in-memory query engine for select-project-join queries on factorised databases. Key components of FDB are novel algorithms for query optimisation and evaluation that exploit the succinctness brought by data factorisation. Experiments show that for data sets with many-to-many relationships FDB can outperform relational engines by orders of magnitude.
Design of Intelligent layer for flexible querying in databases
Mrs. Neelu Nihalani,Dr. Sanjay Silakari,Dr. Mahesh Motwani
International Journal on Computer Science and Engineering , 2009,
Abstract: Computer-based information technologies have been extensively used to help many organizations, private companies, and academic and education institutions manage their processes and information systems hereby become their nervous centre. The explosion of massive data sets created by businesses, science and governments necessitates intelligent and more powerful computing paradigms so that users can benefit from this data. Therefore most new-generation database applications demand intelligent information management to enhance efficient interactions between database and the users. Database systems support only a Boolean query model. A selection query on SQL database returns all those tuples that satisfy the conditions in the query.But lately, there is an overwhelming need for non-expert users to query relational databases in their natural language using linguistic variables and terms instead of working with the values of the attributes. As a result, intelligent databases have emerged, which provides expanded and more flexible options for manipulating queries. In this paper, we propose an intelligent layer for database which is responsible for manipulating flexible queries. Initially, the flexible queries from users in their natural language are submitted to intelligent layer and this layer converts the amorphous query into a structured SQL query. The shaped query is executed and the results are presented to the user. Afterwards, on the basis of results, feedback and the acceptance or rejection of the results are requested from the user. It enables the design of a knowledge based self learning system based the values obtained from user, which will aid the selection of appropriate SQL query, when a same flexible query is issued in the future. The experimental results demonstrate the effectiveness of the proposed intelligent database system.
Developing an Intelligent User Interaction Development Engine
Ashit Kumar Dutta
International Journal of Computer Science Issues , 2011,
Abstract: This paper presents an intelligent user interaction development (IUID) engine that helps to enhance the structure of the relational database by using artificial intelligence. The IUID consists of two phases, the first phase enhances the user database by comparing it with a well structured pre-defined database, and the second phase gives the user the ability to use the artificial intelligence by writing java code and organize it in a tree structure. The main objectives of the IUID engine are to allow user to use the expert knowledge to upgrade his/her database, and increasing the speed of the development process by appending a new artificial intelligence layer at the user application that is represented by a package to run in the application.
INTELLIGENT TRAIN ENGINE FOR THE FASTEST NEW AGE TECHNOLOGY
ANIL KUMAR VERMA,DHARMENDRA KUMAR,GOPAL KRISHNA GOLE,JITENDRA KUMAR
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: This paper describes the design of the intelligent train Engine. These types of engine have capacity to control the train speed in different steps. It is based on the smart timer IC555, IR sensor TSOP1738, MC89C51 the idea is whenever any engine observes a red signal on its track it will start decreasing its speed gradually and stops automatically at some distance from the signal pole. After then when it get green signal the driver can manually start the train and go on. In the mean time when train has not stopped yet and a red signal becomes green then it crosses the signal pole with low speed and then driver can slowly increase the speed.
Design of Intelligent layer for flexible querying in databases  [PDF]
Mrs. Neelu Nihalani,Dr. Sanjay Silakari,Dr. Mahesh Motwani
Computer Science , 2009,
Abstract: Computer-based information technologies have been extensively used to help many organizations, private companies, and academic and education institutions manage their processes and information systems hereby become their nervous centre. The explosion of massive data sets created by businesses, science and governments necessitates intelligent and more powerful computing paradigms so that users can benefit from this data. Therefore most new-generation database applications demand intelligent information management to enhance efficient interactions between database and the users. Database systems support only a Boolean query model. A selection query on SQL database returns all those tuples that satisfy the conditions in the query.
A Taxonomic Search Engine: Federating taxonomic databases using web services
Roderic DM Page
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-48
Abstract: The Taxonomic Search Engine (TSE) is a web application written in PHP that queries multiple taxonomic databases (ITIS, Index Fungorum, IPNI, NCBI, and uBIO) and summarises the results in a consistent format. It supports "drill-down" queries to retrieve a specific record. The TSE can optionally suggest alternative spellings the user can try. It also acts as a Life Science Identifier (LSID) authority for the source taxonomic databases, providing globally unique identifiers (and associated metadata) for each name.The Taxonomic Search Engine is available at http://darwin.zoology.gla.ac.uk/~rpage/portal/ webcite and provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names.Biological taxonomy provides the central link between diverse items of information about an organism. Given the scientific name of an organism, a researcher can query a wide range of databases for information on that organism's genome, development, morphology, geographic distribution, behaviour, phylogeny, and conservation status. However, the utility of taxonomic names as keys to accessing information is hampered by several factors, notably the lack of a single authoritative list of all taxonomic names [1,2]. In the absence of such a list, databases that make use of taxonomic names have no ready means of validating those names. Consequently, there is no guarantee that taxonomic names stored in different databases will be mutually consistent.In the absence of a single database of names, one solution is to use a federated approach [3] where multiple, independent databases are queried. Numerous taxonomic databases exist, although each tends to have limited taxonomic and geographic scope, and the degree of interoperability among these databases varies greatly. The NIH/NIAID/Wellcome Trust Workshop on Model Organism Databases [4] defines the minimum level of interoperability as providing a FTP dump of the database contents. The only taxonomic database
Design of Intelligent SoC Controller for Engine Oil Sensing and Monitoring System  [PDF]
Mohd Faizul Idros,Sawal Hamid Ali,Shabiul Islam
Asian Journal of Scientific Research , 2012,
Abstract: The demand for engine oil monitoring system has increased recently due to the awareness of used engine oil pollution and as a cost reduction measure for the customer. Previously, many automotive manufacturers advised the customers to change their engine oil at a constant time (i.e., during service) or according to the mileage interval. There is a possibility that the engine oil is changed before the end of its lifetime which will incur higher maintenance cost to the customer. Furthermore, this situation will give bad input to the environment due to the excessive engine oil waste. This review paper described a potential mechanism for engine oil monitoring so that the oil is changed when deemed necessary. The research consists of data analysis, statistical analysis, intelligent system development, System on Chip (SoC) design, fabrication and testing. A statistical analysis of Multiple Linear Regression is used to predict the worst condition of the engine oil. Total Acid Number (TAN), Total Base Number (TBN), viscosity and oxidation level were chosen to be the main parameter for condition based monitoring purpose. A complete SoC design with Verilog Hardware Description Language (HDL) and FPGA implementation has been reviewed as a potential technique for the engine oil monitoring and changing time prediction system.
Sagace: A web-based search engine for biomedical databases in Japan
Mizuki Morita, Yoshinobu Igarashi, Maori Ito, Yi-An Chen, Chioko Nagao, Yuki Sakaguchi, Ryuichi Sakate, Tohru Masui, Kenji Mizuguchi
BMC Research Notes , 2012, DOI: 10.1186/1756-0500-5-604
Abstract: We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry.Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at http://sagace.nibio.go.jp/en/ webcite.Modern biomedical research produces increasing amounts of data, much of which is stored in numerous public databases. (Some of these databases are described in the Database Issue of Nucleic Acids Research each year [1]). As life sciences become ever more data-driven, there is great potential for mining multiple different databases and generating a new knowledge. The sheer number of databases, however, makes data integration a formidable task.To tackle this issue, the Database Center for Life Science (DBCLS; [2]) and the National Bioscience Database Center (NBDC; [3]) were established in Japan in 2007 and 2011, respectively, with the mandate to archive and integrate Japan’s life sciences databases. In an effort to promote effective data integration, they compiled a database list and developed a framework for distributed search systems, based on which, designated national centers can create domain-specific search websites. The indexes for the selected databases were created by NBDC and other designated national centers, including the National Institute of Biomedical Innovation (NIBIO; [4]).In close collaboration with the DBCLS and the NBDC, we at the NIBIO have developed a search web site called ‘Sagace’ (Figure? 1), as a first step towards
Intelligent Implementation Processor Design for Oracle Distributed Databases System  [PDF]
Fadoua Hassen,Amel Grissa Touzi
Computer Science , 2015,
Abstract: Despite the increasing need for modeling and implementing Distributed Databases (DDB), distributed database management systems are still quite far from helping the designer to directly implement its BDD. Indeed, the fundamental principle of implementation of a DDB is to make the database appear as a centralized database, providing series of transparencies, something that is not provided directly by the current DDBMS. We focus in this work on Oracle DBMS which, despite its market dominance, offers only a few logical mechanisms to implement distribution. To remedy this problem, we propose a new architecture of DDBMS Oracle. The idea is based on extending it by an intelligent layer that provides: 1) creation of different types of fragmentation through a GUI for defining different sites geographically dispersed 2) allocation and replication of DB. The system must automatically generate SQL scripts for each site of the original configuration.
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