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In recent years, the rapid growth of the Internet has greatly changed our way of sharing information. Internet brings both challenges and opportunities to modern enterprise information system. In this paper, we describe advanced data service architecture for modern enterprise information system. This architecture solves two main issues: semantic integration of data and adaptability of data server...
It is widely recognized that OLTP and OLAP queries have different data access patterns, processing needs and requirements. Hence, the OLTP queries and OLAP queries are typically handled by two different systems, and the data are periodically extracted from the OLTP system, transformed and loaded into the OLAP system for data analysis. With the awareness of the ability of big data in providing enterprises...
By analyzing the problem and key technology about integration of heterogeneous database, a distributed database integration model with global schema based on multi-agent technology is proposed. Firstly, distributed database (DDB) global schema and fragment and assignment schema are designed according to actual instance of production organization. Secondly, multi-agent system of administration architecture...
With network developed quickly and used widely, how to store mass data has become a problem we have to face. Traditional methods usually add more servers into to increase computing speed and storage ability, while the hardware cost is very expensive and the storage efficiency is low. Through deep study of MapReduce programming model and Hadoop framework, this paper presented a mass data storage model...
Analyzing the problem and key technology about integration of heterogeneity database, a distributed database integration model with global schema based on multi-agent technology is put forward. Firstly, DDB global schema and fragment and assign schema is designed according to actual instance of production organization. Secondly, multi-agent system of administration architecture is designed. Thirdly,...
Map/reduce is a popular parallel processing framework for massive-scale data-intensive computing. The data-iterative application is composed of a serials of map/reduce jobs and need to repeatedly process some data files among these jobs. The existing implementation of map/reduce framework focus on perform data processing in a single pass with one map/reduce job and do not directly support the data-iterative...
In view of the shortcomings of traditional distributed technologies such as CORBA, DCOM +, J2EE in achieving information sharing and exchange among autonomy, distributed, heterogeneous data sources, a U-B-M three-layer information integration architecture is presented. This architecture is applied to information integration platform for gas fields with MAS, the virtual data center framework based...
Nowadays, more and more databases are distributed in networks. To produce knowledge with the databases, traditional data mining technology should be developed in model, algorithm, strategy, etc. The popular distributed data mining (DDM) systems are introduced and classified into three classes: DDM systems based on parallel data mining agents, DDM systems based on meta-learning, and DDM systems based...
This study's objective is to solve mining knowledge issue from different sources of distributed, formatted or unformatted data with diverse data semantics. The issue is formulated as data mining in knowledge grid. In particular, this paper presented a DM-GRID (data mining in grid) model based on software architecture of a novel infrastructure for distributed and high-performance data mining in knowledge...
In this paper, we propose an autonomic management framework (ASGrid) to address the requirements of emerging large-scale applications in hybrid grid and sensor network systems. To the best of our knowledge, we are the first who proposed the autonomic sensor grid system concept in a holistic manner targeted at non-trivial large applications. To bridge the gap between the physical world and the digital...
Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified...
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