Research of Smart Space based on Business Intelligence 1 Jia-yi YAO, 2 Tian-tian MA 1 School of Economics and Management, Beijing Jiaotong University, jyyao@bjtu.edu.cn 2 School of Economics and Management, Beijing Jiaotong University, 09120715@bjtu.edu.cn doi : 10.4156/aiss.vol3.issue6.22 Abstract In the field of smart space, due to the development of technology for remote sensing, monitoring, a vast volume of spatial data is accumulated. However, there have many problems of discovering meaningful knowledge from the spatial data. Consequently, in this paper, a scientific and effective method that spatial information is intelligently processed in smart space by BI was put forward. It describes the development of smart space and the technologies of BI. Then, it presents a general integration mode, which integrates smart space and BI technology. 1. Introduction Keywords: Business Intelligence, Smart Space, Integration Mode Due to the development of information technology, the ability to process and use information has been becoming a key factor for enterprises. BI technology has reached the point of maturity in enterprises, currently it has a great practical significance for researching how to use BI technology to enhance the capabilities of information processing and strengthen the effectiveness of decision-making. With the revolution of ubiquitous/pervasive computing emerging [Suo and Shi, 2008], smart space was presented. In the field of smart space, due to the development of technology for remote sensing, monitoring, GPS, WSN systems and GPS, a vast volume of spatial data is accumulated. However, there have many problems of discovering meaningful knowledge from the spatial data, such as the capacity of spatial knowledge discovery and spatial decision-making is weak, no meeting the demand of the social and regional sustainable development in spatial analysis and decision support, and so on. Although various promising features have been achieved and presented in Smart Space, there is still little research on some of the essential issues for spatial information processing. Consequently, in this paper, a scientific and effective method that spatial information is intelligently processed in smart space based on BI technology was put forward. That could improve the efficiency of smart space in acquiring spatial information and application, make decisions more humanized and intelligent. Being an initiative work, this paper mainly focuses on building the system framework for smart space based on BI. 2. Overview of Smart Space A smart space is a multi-user, multi-device, dynamic interaction environment that enhances a physical space by virtual services [Wang et al., 2004]. Smart spaces are open, distributed, heterogeneous pervasive computing systems. Researches on smart space keep active in recent years so that a variety of studies have been proposed to deal with different aspects of smart space such as information collection, context-aware computing, middleware and safety. However, studies have also suggested, from the point of view of socioeconomic, that smart space may face more severe challenges. Understanding the main characteristics of smart space in application, we encounter interesting questions which are essential to it: Open space; With the further development of pervasive computing, smart space must gradually transformed from the separate smart spaces into open smart spaces, and finally into more intelligent smart spaces of - 181 -
interconnected smart community. A vast volume of spatial data from this smart community cannot be used effectively in fact because of their complex correlation and too large scale. Particularly, it should be noted that information sharing between multiple smart spaces is an important function, and interconnection among spaces, access to resources and spatial data acquisition will be essential problems of smart space. Coupling degree. From the smart space point of view of the various component systems, these smart spaces only as an independent module were dealt with when they were transforming, not as a system. Actually, the coupling degree is lower between multiple smart spaces [Meier et al., 2009]. That will not only lead to repeated data acquisition, but also lead a large amount of spatial information collected from smart spaces not to be fully utilized. Above all, it is an urgent need for effective and efficient methods to extract unknown and unexpected information from spatial data sets of unprecedentedly large size, high dimensionality, and complexity in smart space. 3. Business Intelligence Architecture BI systems are rapidly being adopted to provide enhanced analytical capabilities to previously installed ERP systems, which manage and integrate a very large array of business information. BI systems are defined as specialized tools for data analysis, query, and reporting, (such as OLAP and dashboards) that support organizational decision-making. 3.1. BI System Figure 1. An overview of the system architecture of BI - 182 -
BI is a new integrated system based on modern management theory and supported by information technology. The core technologies of BI are data warehouse (DW), data mining (DM) and on-line analytical processing (OLAP).In addition, BI system includes data visualization technology and frontend display that must be flexible, and favourable human-computer interface that has the ability to provide better interpersonal interface, to complement and support the business decisions of the whole process. Figure 1 provides an overview of the system architecture of BI. Just as shown in Figure 1, it contains following functions: collecting and reading data, presenting a convenience exhaustive inquiry and analysis function, data mining and making the result visible. 3.2. The Core Technologies The core technologies of BI are DW, DM and OLAP, actually, there are technologies such as spatial database and spatial data warehouse (SDW) technology. 3.2.1. Spatial Data Warehouse Spatial data warehouse (SDW) based on the combination of GIS technology can not only integrate various spatial information from different spatial databases, but also offer nice information service for spatial decision and knowledge discovery. SDW system considers how to effectively manage spatial information and providing spatial on-line analytical services. At present, there are two types of SDW solutions. An association scheme based on GIS and SDW [Moura et al., 2002]. The association scheme based on GIS and SDW is shown in Figure 2. This approach directly based on relational database is easy to implement, but it has a limited ability of analysis. Besides, it is hard to maintain system reliably and update the data effectively when there is a large amount of data. An association scheme based on middleware. In this situation, the solution associated with the independent modules makes it easy to access and maintain in a database application, but thus do not easily allow spatial data to integrate, index, manage and analyze without middleware. 3.2.2. Spatial Data Mining Modules Figure 2. Architecture based on GIS and SDW Spatial data mining (SDM) regarded as a spatial extension of DM is able to extract the spatial patterns and characteristics, the summary relationship between the spatial and non-spatial data and other summary data characteristics which are not explicitly stored, but for the people are interesting from the massive spatial data. SDM has functions for extracting of patterns of spatial and non-spatial attributes. A process of SDM by comparison and reference is depicted in Figure 3. The SDM module processes respective data mining functions and transfers the results of data analysis to the knowledge base management supporting easy communication between the SDM - 183 -
module and the interface. This module provides four DM functions including spatial clustering, spatial classification, spatial characterization and spatio-temporal association rule mining. Human-computer interaction interface SDM module Knowledge discovery Spatial data SDW management module Knowledge base management module Spatial database SDW Others Knowledge base Figure 3. The process of SDM 4. Integration Mode of Smart Space Based on BI In this section, problems of existing smart space explained in Section 2 are discussed and refinements applied for them will be explained. 4.1. Integration mode of smart space and BI Smart space mainly through GIS, RS and GPS technology acquires spatial information and makes spatial decisions. And SDM has been widely used in GIS, RS, image processing and guidance. Consequently, the scientific and effective method that spatial information is intelligently processed in smart space system based on BI technology is put forward in order to refine existing smart space. The system framework of smart space based on BI technology integrating GIS, RS and GPS technology and SDW, SDM and SOLAP technology is proposed. Figure4. Smart space based on BI technology - 184 -
Figure 4 illustrates the mechanism for this architecture that can exploit knowledge extracted by the use of SDM techniques, make decisions more humanized and intelligent, and provide personalized service. While users carrying their mobile devices can easily interact with the smart space, and propose the problem through the human-computer interaction interface. Then the BI module triggers the data mining core, obtains valuable knowledge from the spatial database, image database, GIS database and other databases, and finally provides the knowledge and analysis results to the users through the human-computer interaction interface. 4.2. The major application There are three aspects of the major application of knowledge discovery in smart space based on BI technology. Analyzing mass of spatial data in smart space intelligently; GIS database, the spatial database, image database and other databases based on smart space and SDM integration can more effectively extract the required data, carry through data handling operations based on this, mine the knowledge hidden behind the large amounts of data by using mining algorithm, and then provide the information for decision making[wang et al., 2009]. Compared with the existing analysis tools of GIS, acquiring knowledge and information through SDM was highly improved, and SDM is able to extract the knowledge, model or rule which cannot be acquired by GIS analysis tools from the massive spatial data which is innovative, interesting, hidden, unknown, potentially useful and ultimately understandable. The range of GIS applications goes beyond the automating cartography, but it is extended by adding knowledge-base and data mining capabilities to the systems for analyzing mass of spatial data in GIS intelligently. Application in GPS and remote sensing image; SDM is the core techniques and tools for establishing expert system of remote sensing image, while the results of remote sensing image can update GIS database. New emerging technologies of BI, especially SDW and SDM technologies will outstand as a more efficient and feasible integration of GIS, RS and GPS. 5. Conclusions The technologies of smart space and BI technology, especially SDM technology, as the two different strategies method of analysis of spatial data exploration, it is very useful to integrating them together, to form a multimodal interactive model that assists humans to efficiently complete tasks or decision-making by offering abundant information and assistance. This is a very young and promising field, the work is essential for establishing an infrastructure to help combine smart space with BI technology. 6. References [1] Zang Li, Chao-Hsien Chu, Wen Yao, Behr, R.A., Ontology-Driven Event Detection and Indexing in Smart Spaces,Semantic Computing (ICSC) 2010 IEEE Fourth International Conference, pp.285-292, 2010. [2] Balandin S., Oliver I., Boldyrev S., Smirnov A., Shilov N., Kashevnik A., Multimedia services on top of M3 smart spaces, Computational Technologies in Electrical and Electronics Engineering (SIBIRCON), 2010 IEEE Region 8 International Conference, pp.728-732,2010. [3] Liampotis N., Roussaki I., Papadopoulou E., Abu-Shaaban Y., Williams M.H., Taylor N.K., McBurney S.M., Dolinar K., A Privacy Framework for Personal Self-Improving Smart Spaces, Computational Science and Engineering, 2009. CSE '09. International Conference, pp.444-449, 2009. [4] Kuusijarvi Jarkko, Evesti Antti, Ovaska Eila, Visualizing structure and quality properties of Smart Spaces, Computers and Communications (ISCC), 2010 IEEE Symposium, pp.1-3, 2010. - 185 -
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