Analysis on Data Mining Model Objected to Internet of Things

Analysis on Data Mining Model Objected to Internet of Things School of Computer & Communication Engineering, University of Science & Technology Beijing No.30, Xue Yuan Road, HaiDian District, Beijing, 100083, CHINA Email:Chunguang_zh@163.com Abstract Three data mining models suitable to data processing have been proposed according to some technical challenges on the characteristics data for Internet of Things. The first model is the multilayer data mining model, which consists of data collection, data processing, event processing and data mining service. The second model is distributed data mining model that could be used to solve the problem of data stored in different locations. The third model is grid-based data mining model, which could utilize the potentially unlimited amount of data by using grid. At the same time, the paper learns from decomposition - build "theory to solve the data mining problem in the internet of things. Finally, the key issues are discussed about model application. 1. Introduction Keywords: Internet of Things, Data Mining, Distributed, Grid, Service According to the contractual agreement, Internet of things connects any items with Internet, implements information exchange and communicates through information sensing equipment such as the sensor, radio frequency identification (RFID) and global positioning system. Internet of things needs be designed to identify, supervise and monitor items so as that it can provide various types of information services for users innovatively. The Internet of things has very complex data types, including sensor data, RFID data, two-dimensional code, video data, audio data, and image data. For example, a supply chain using RFID technology is adopted by a supermarket. The original data of the RFID are EPC, location, and time. Where EPC represents the sole identification of a RFID reader, location indicates the position of the reader, time infers to the reading time. The data requires 18 bytes to be stored as an RFID record. There are about 700000 RFID records for a supermarket. If there is a reader at any second in the supermarket, there will be 12.6GB RFID data stream in one second and 544TB data for one day. Thus, database creation of and information collection of Internet of Things has real-time and continuous characteristics. The amount of data continues to increase with time and is of potential infinity. These characteristics have brought new challenges for the data mining technology. As shown in Figure.1. Figure 1. Challenges of Data Characteristic in Internet of Things International Journal of Advancements in Computing Technology(IJACT) Volume4, Number21,November 2012 doi: 10.4156/ijact.vol4.issue21.73 615

It is very necessary to manage, analyze and mine data by using effective ideas. Now research on mining data in Internet of Things mainly includes three aspects: (1) Be centralized to manage and mine RFID data stream. For example, Hector Gonzalez, etc. Leterature [1] has proposed storage RFID data model that can protect the object changes and total amount of compression, and path dependence. The RFID cube has three tables: the information table can store up the path dependency information of the product; the pause table can store up the position information of the data; the graph table can store up the routing information of the structure analysis. The model uses the flow chart to represent the transport of goods and achieve multi-dimensional analysis of commodity flow. In the literature [2], it proposed a compression probability workflow that can capture movement and important flow RFID exception. Elio Masciari has studied isolated point mining in the RFID data stream. (2) Query, analyze and mine object data movement produced by all kinds of service in Internet of Things such as GPS equipment, FID sensor network, network radar and satellite etc. Jae-Gil Lee, et al. proposed a roaming framework for anomaly detection of moving objects. In the Literature [3], Jae-Gil Lee et al. developed a split detection framework about the track isolated point detection of movement objects. Jae-Gil Lee et al.developed a partition-and-group framework about trajectory clustering of movement objects. (3) Knowledge discovery of sensor data. Sensor networks have several characteristics such as limited resources, easy to deploy sensors, maintenance-free, multi-layer jump and large amounts of data and so on. Betsy George, et al.[4] proposed a data model named Spatio-Temporal Sensor Graphs (STSG) for the discovery of Spatio-temporal patterns. STSG can find different types of abnormal position modes. Parisa Rashidi[5] proposed a adaptive mining framework about sensor data type to adapt to changes in the data. Data mining has been removed from the traditional data statistics and analysis, potential models discovery and mining. It has become indispensable tool and links in Internet of Things. It has brought about a number of technical challenges and issues. First of all is a real-time high performance data mining, when faced with a rapidly changing environment any control port will need to make real-time analysis and decision. Second is distributed data mining, it is necessary to adopt a distributed parallel data mining because computing equipment and data are natural distribution in Internet of Things. Third is data quality control, multi-source, multi-modal, multi-media, multi-format data storage and management is an important guarantee to control data quality, and get real results. Last is the decisionmaking control. Mining models, rules and the characteristics indicator are used to predict, make decision and control. To solve these problems, this paper proposes the following three kinds of data mining models on Internet of Things. 2. Data mining models in Internet of Things 2.1. Multi-layer data mining model in Internet of Things According to hierarchical structure of Internet of Things and RFID data mining framework [10], multi-layer data mining model is proposed and divided into four layers: data collection layer, data management layer, event process layer and data mining service layers. As shown in Figure 2. Data collection layer is also called the perception layer. It functions more like human nerve endings, such as, people s eyes, ears, nose, and throat. It is the source for IOT to recognize things and collect information. Its main functions are recognizing things and collecting information. Data collection layer use some equipment such as RFID reader, receiver and so on(we called Q&A) to collect the data of all kinds of smart objects such as RFID stream data, GPS data, satellite data, location data and sensor data and so on. Different types of data require different collection strategies. Q&A collect information (temperature, humidity and image etc.) and transmit it to the upper gateway access points by itself networking, then those information collected by the gateway access submit to the data manage layer through network. During the data collection process, a range of issues such as energy saving, misreading, repeatable read, fault tolerance, data filtering and communications, should be properly resolved. 616

The data management layer can manage collected data from centralized or distributed database or data warehouse. After target recognition, data abstraction and compression, a series of data is stored in the database or data warehouse. For example, the RFID data, the original RFID data format is EPC, the position, the time, in which EPC is marked for intelligent object ID [7]. After data is cleaned, data that contains the pause table records is obtained. After using data warehouse to store and manage data, including the information table, the pause table and the graph table, namely RFID body. Based on the RFID body, users can easily analyze and process RFID data online. In addition, XML languages can also be used to describe Internet of Things data. Smart objects can be connected each other through data management layer in Internet of Things. Figure 2. Multi-layer Data Mining Model in IOT The event is the integration of data, time and other factors, so it provides a high level of processing mechanism in Internet of Things. The event-processing layer is used effectively to analyze events and realize inquiry analysis based on the event. Event process layer is in the middle of the data management layer and the event mining service layer. It received data from the lower layer to provide specific services to the upper layer. The lower layer is transparent for the upper. While some factors triggered specific event, the event process layer would get and place data that acquired from the data management layer to primary event module. Event filtering is the most important part of the event process layer. Through various data mining strategy, it would get the content hidden in the data. And then congregate, organize and analyze data according to the event. Event detection is mainly responsible for the response for the upper probe queries and the results provided to the upper layer. The data mining service layer is built on the basis of the data management and event processing. The data mining service layer provides specific service to users through the analyzed and processed the data. The data mining service consists of data, data mining, knowledge three parts. Data refers to the data from the lower transmission. Here data mining emphasized the extraction of characteristics. Knowledge represents a specific service. A variety of object-based or event-based data mining, classification, prediction, clustering, outlier detection, correlation analysis, or types of mining are provided to the application. 617

2.2. Distributed data mining model in Internet of Things Compared with common data, the data in Internet of Things are always massive, distributive, timerelating and location-relating, also, the sources of data are distinctive and the resources of the nodes are limited [8]. All of the features above bring in many problems about central data mining. (1) A lot of data in Internet of Things are stored in different places so that it is very difficult to mine distributed data. (2) Massive data in Internet of Things need a real-time processing. So a pretty high demand will be needed if central construction is adopted. (3) It is not a viable strategy to put all the relevant data together considering some aspects on data like, safety, privacy fault-tolerance, commercial competition, restriction of law and others. (4) The nodes resources are limited. It doesn t optimize the transition of the expensive resources to lay all the data on the central joint. In most cases, the central joint doesn t need all the data, but need to estimate some parameters [9]. Therefore, we should preprocess the original data in the distributive joint, and then send necessary information to receivers. We propose a distributed data mining model. It is shown in Fig 3. Not only can we use this model to solve distributed problems brought about by the storage node, but also simplify complex problems. So High-performance requirements, high storage capacity and computing power will be reduced. Figure 3. Distributed Data Mining Model in Internet of Things The global control node is the core of the data mining system in this model. It selects data mining algorithm and organizes mining data sets, then guides these sets to secondary nodes, which will collect original data from various kinds of smart objects. The original data will be stored in the local database after preprocess of data filter, abstraction and compression. A local model is obtained with event filter, complex event detection and data mining of local node. When needed, the local model will be controlled by the global control node, and all set of local models will form the global model. Data of objects, preprocessed data and information can be exchanged among secondary nodes. Multi-layer of agents based on union administrative mechanism controls the whole process. 2.3. Data mining model based on grid in Internet of Things Grid computing is a new type of computing devices, which are able to realize the heterogeneous, large-scale and high performance applications. The basic idea of the grid is using the grid computing resources like electric power. All kinds of computing resources, data resources and the service resources can be accessed or in convenient use. The basic idea of Internet of Things is that through Internet connected to all kinds of intelligent object, this object will become clever, intelligent sensitive 618

environmental and remote share, so we can regard intelligent object as a grid computing resources, using grid data mining service to realize mining operation of Internet of Things data. P.Brezany et al. [10] proposed one infrastructure named GridMiner which support online decentralized analysis processing and data mining. D. Bruckner describes a novel architecture for surveillance networks based on combining multimodal sensor information [11]. In this article, we propose a data mining model based on grid in Internet of Things according to data mining grid proposed by stankovski.[12].this can be seen in Figure 4. Figure 4. Data Mining Model Based on Grid in Internet of Things Data mining model based on grid in Internet of Things consists of five layers, respectively, which is IOT resource layer, IOT service layer, grid middleware layer, grid mining layer, grid application layer from bottom to top. IOT resource layer broke the original definition of the grid and Equivalent emphasis on software and hardware. Hardware module includes sensor & GPS, memorizer, database/file, server. Software module includes network resource pool, storage resource pool, data resource pool, server resource pool. Hardware itself is also a shared resource in data mining model based on grid in Internet of Things. Here Sharing is not narrow the sharing of a function of the hardware but the hardware itself can be shared. IOT service layer is a fine division for various services. From the viewpoint of the objectoriented, the main purpose of data mining is to provide users with a more accurate, timely, convenient service. These services can be composed by some small service function modules. So, IOT service layer presumably includes computing service, network service, storage service, data service, scheduling service, service transmission and so on. Grid middleware layer is mainly to solve a series of problems generated by a network of heterogeneous. IOT is a large and complex huge system. Various network hardware, software, protocol exist in IOT. Using grid middleware layer can efficient reduce the complexity. Grid mining layer is primary responsible for various data fusion. The data fusion method is very much. The paper lists four kinds, including information service, data service, mining service agent, mining service center. The uppermost layer of the model is Grid application layer. It provides users with intelligent service. The figure lists four module, data manipulator, workflow controller, execution manager, IOT application. The difference between Data mining model in Internet of Things and Grid Data Mining can be a part of hardware and software. Internet of Things can provide various hardware supports such as RFID label, RFID Reader, WSM, WSAN as well as Sensor network and so on. It also provided many software resources such as event processing algorithm, data warehouse and data mining application and 619

so on. The high-standard service from grid data mining can be fully used to complete the data mining in Internet of Things.[13]. 3. Analysis on key issues about model 3.1. Collect data from smart objects in Internet of Things The Internet of things is "connected to the Internet ". This has two meanings. Firstly, the core and foundation of the internet of things is still internet, and it is the extension and expansion of the internet. Secondly, it advocates is to achieve any thing, any time, any place of information exchange, and ultimately to achieve information sharing. So, the information collection is a key step in the Internet of Things. When we collect data from intelligent objects, we need consider the special requirements of the intelligent object. For example, if we want to collect data from distributed sensor network, you should consider network efficiency, scalability and fault tolerance. A series of strategies, such as regional data sets can be used. Therefore, the amount of data transmission will be reduced, and the sensor nodes that use energy will be improved. In order to adjust the goal and conflict during the mining process of sensor network data, the literature [12] put forward a general probability framework under the limit of calculation, electric power, and memory. 3.2. Data abstraction, compression, indexing, aggregation, and multi-dimensional query There will be great smart objects data existing in Internet of Things. How to manage the data and how to quick perform analysis and disposal on line should be taken into consideration. Data of smart objects are of owned characteristics, these are: great data stream will be produced by RFID and sensor devices in Internet of Things; data of smart objects may be inaccurate, and are of time and position related normally; data of smart objects are of implication usually. In order to adapt these characteristics, new demands are needed for data managing and data mining of Internet of Things. The points are as follows: (1) Smart object recognition and addressing: In Internet of Things, there will be tens of thousands of smart objects entities. In order to find and connect these smart objects, it is necessary to identify and address the smart objects. (2) Data abstraction and compression: effective ways to filter redundant data should be developed. (3) Data archiving, indexing, scalability and access control for Internet of Things. (4) Multidimensional analysis of the query language is needed for data warehouses. (5) Connectivity and Semantic understanding on the heterogeneous data in Internet of Things. (6) Timing level and event-level data collection. (7) Privacy and protection issues for management of Internet of Things data. 3.3. Event filtering, aggregation, and detection The internet of things is a large and complex system. How accurately and quickly to deal with these data became a key issue that the internet of things must be solved. If the data acquisition ways are different, the characteristics of the data are also different. Part of the method of processing data of the original Internet can also be used for the Internet of things, so it can be transplanted. But also part of the problem is that the Internet does not come across, therefore, new approaches that can be used to resolve those problems must be research. Using the Tu Xuyan Professor solves large system "decomposition - build" method of dealing with this problem for Reference, Complex events in the internet of things is decomposed into event filtering, aggregation and detection.[14] Event filtering and complex event processing can be used to handle simple event. The procedure includes: data of events are collected firstly; valuable events are kept after events filtering then, the set of simple events are added to the set of complex events finally. Therefore, the logical relationship among business can be obtained by detecting complex events. For example, Tai Ku, et al has put forward a new event mining structure, and defined the basic concept of event administrative on supply chain under RFID technical frame. 620

3.4. Comparison of centralized and distributed data processing and mining For different occasions, one should make flexible use of centralized or distributed data processing and mining models. Distributed sensor networks, for example, could send all the data to the Meeting Point based on limited computing node and storage, power constraints. However, such strategy cannot optimize the transmission of expensive energy. In fact, they, in most cases, do not need all of the raw data. Instead, they only focus on the value of some parameters. 3.5. Research on data mining algorithm Based on data management and event processing of Internet of Things, the key question is learning new mining algorithm for its data. The main work includes classification and prediction, clustering, outlier detection, relationship analysis, space and time model mining. For instance, Deng et studied frequent closure circuit algorithm of data mining in RFID application. Tom studied outlier detection from RFID data stream. 4. Data Mining Model Objected to Internet of Things Through the above analysis, we gave an instance of internet of things application layer data mining model. As see figure 5.The model can be a good solution to the data mining of the application of the internet of things. Through the above analysis, we gave an instance of internet of things application layer data mining model. As see figure 5.The model can be a good solution to the data mining of the application of the internet of things. Database W e b Documents Integration Service O ntology Libaray Extracting context inform ation Labeling context inform ation K now ledge base O n to lo g y Libaray M atching query Extending query Context in fo rm atio n Context in fo rm a tio n M atching results F ilterin g resu lts Sorting results Context in fo rm a tio n C o m b in in g q u ery Subm itting keyw ords Integration Service user Figure.5. Data mining model of application-layer of Internet of Things 621

5. Conclusion As a significant direction for the development of next-generation Internet, Internet of things has attracted the attention of industry and academia. The data in Internet of Things has many features, such as distributed storage, a lot of relevant data on time and place, and limited node resources, etc., which are also challenging tasks for data accessing in Internet of Things. We propose three kinds of data mining models and discuss the significant problems in Internet of Things. Aiming at the characteristic of massive data in Internet of Things, it is necessary to further explore the simple, efficient and reliable mass data storage technology. It is necessary to take full consideration of removing redundancy and keeping its integrity and authenticity as much as possible in data preprocessing of Internet of Things. Since Internet of Things is a distributed and heterogeneous integration, we need further study the data reduction and extraction technologies so that knowledge feature data sets can be efficient and fully excavated. To simplify massive data mining, we will focus on considering problems about the complexity and efficiency of mining algorithm. 6. Reference [1] Hector Gonzalez, Jiawei Han, Xiaolei Li,Diego Klabjan, Warehousing and Analyzing Massive RFID Data Sets, In Proceedings of the 22nd ICDE Symposium on Data Engineering,pp.83-87,2006. [2] Hector Gonzalez, Jiawei Han, Xiaolei Li, Mining compressed commodity workflows from massive RFID data sets, In Proceedings on Information and Knowledge Management (CIKM 06),pp.162-171,2006. [3]Jae-Gil Lee, Jiawei Han,Xiaolei Li, Trajectory outlier detection: A partition-and-detect framework,in Proceeding of the 24th Int'l Conf. on Data Engineering, pp.140-149, 2008. [4] Pradeep Mohan, Shashi Shekhar, Fellow, IEEE, James A. Shine, James P. Rogers, Cascading Spatio-Temporal Pattern Discovery, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 24, no. 11,pp.1977-1992,2012. [5]Parisa Rashidi and Diane J. Cook. An Adaptive Sensor Mining Framework for Pervasive Computing Applications, 2nd International Workshop on Knowledge Discovery from Sensor Data, 2008. [6]Ma Yan-mei, Ren Hong-e, Application of RFID and Data Mining in the Timber Management System,International Conference on Control, Automation and Systems Engineering (CASE), pp.1-4,2011 [7]M. Langheinrich, A Survey of RFID Privacy Approaches, Personal and Ubiquitous Computing,vol.13,no.6,pp.423-421, 2009. [8]Yang Lvqing, "Analysis and Design of Campus Safety Management System based on Internet of Things", JCIT, Vol. 7, No. 15, pp. 400 ~ 408, 2012 [9]Kun Gao, Qin Wang, Lifeng Xi, "Controlling Moving Object in the Internet of Things", IJACT, vol.4, no.5, pp. 83-90, 2012 [10]Peter Brezany, Ivan Janciak,A Min Tjoa, GridMiner: a fundamental infrastructure for building intelligent Grid systems, Proc. 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 05),pp.150-156,2005. [11]Dietmar Bruckner,Cristina Picus, Rosemarie Velik, Wolfgang Herzner,Gerhard Zucker Hierarchical Semantic Processing Architecture for Smart Sensors in Surveillance Networks, IEEE transactions on industrial informatics,vol.8,no.2, pp. 291-301,2012 [12]Vlado Stankovski,Martin Swain,Valentin Kravtsov,Thomas Niessen, Digging Deep into the Data Mine with DataMiningGrid, IEEE Computer Society,vol.12,no.6, pp.69-76,2008. [13]Shen Bin, Liu Yuan, and Wang Xiaoyi, Research on Data Mining Models for the Internet of Things, International Conference on Image Analysis and Signal Processing,pp.127-132,2010 [14]Xu-yan Tu, Cooperative Intelligent Modeling & Cooperative Intelligent Simulation, Computer Simulation,Vol.28,no.9,pp.181-185,2011 622