In this paper, we propose four data mining models for the Internet of Things, which are multi-layer data mining model, distributed data mining model, Grid based data mining model and data mining model from multi-technology integration perspective. Among them, multi-layer model includes four layers: 1) data collection layer, 2) data management layer, 3) event processing layer, and 4) data mining service layer. Distributed data mining model can solve problems from depositing data at different sites. Grid based data mining model allows Grid framework to realize the functions of data mining. Data mining model from multi-technology integration perspective describes the corresponding framework for the future Internet. Several key issues in data mining of IoT are also discussed.

Research on data mining models for the internet of things

In the internet-based e-business environment, most business data are distributed, heterogeneous and private. To achieve true business intelligence, mining large amounts of distributed data is necessary. Through a thorough literature review, this paper identifies four main issues in distributed data mining (DDM) systems for e-business and classifies modern DDM systems into three classes with representative samples. To address these identified issues, this paper proposes a novel DDM model named DRHPDM (Data source Relevance-based Hierarchical Parallel Distributed data mining Model). In addition, to improve the quality of the final result, the data sources are divided into a centralized mining layer and a distributed mining layer, according to their relevance. To improve the openness, cross-platform ability, and intelligence of the DDM system, web service and multi-agent technologies are adopted. The feasibility of DRHPDM was verified by building a prototype system and applying it to a web usage mining scenario.

Distributed data mining for e-business

IntroductionComputer science applications are becoming more and more network centric, ubiquitous, knowledge intensive, and computing demanding. This trend will result soon in an ecosystem of pervasive applications and services that professionals and end-users can exploit everywhere. Recently, collections of IT services and applications, such as Web services and Cloud computing services, became available opening the way for accessing computing services as public utilities, like water, gas and electricity.Key technologies for implementing that perspective are Cloud computing and Web services, semantic Web and ontologies, pervasive computing, P2P systems, Grid computing, ambient intelligence architectures, data mining and knowledge discovery tools, Web 2.0 facilities, mashup tools, and decentralized programming models. In fact, it is mandatory to develop solutions that integrate some or many of those technologies to provide future knowledge-intensive software utilities. The Grid paradigm can represent a key component of the future Internet, a cyber infrastructure for efficiently supporting that scenario.Grid and Cloud computing are evolved models of distributed computing and parallel processing technologies. The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. In the area of Grid computing a proposed approach in accordance with the trend outlined above is the Service-Oriented Knowledge Utilities (SOKU) model that envisions the integrated use of a set of technologies that are considered as a solution to information, knowledge and communication needs of many knowledge-based industrial and business applications. The SOKU approach stems from the necessity of providing knowledge and processing capabilities to everybody, thus supporting the advent of a competitive knowledge-based economy. Although the SOKU model is not yet implemented, Grids are increasingly equipped with data management tools, semantic technologies, complex work-flows, data mining features and other Web intelligence approaches. Similar efforts are currently devoted to develop knowledge and intelligent Clouds. These technologies can facilitate the process of having Grids and Clouds as strategic components for supporting pervasive knowledge intensive applications and utilities.Grids were originally designed for dealing with problems involving large amounts of data and/or compute-intensive applications. Today, however, Grids enlarged their horizon as they are going to run business applications supporting consumers and end-users. To face those new challenges, Grid environments must support adaptive knowledge management and data analysis applications by offering resources, services, and decentralized data access mechanisms. In particular, according to the service-oriented architecture (SOA) model, data mining tasks and knowledge discovery processes can be delivered as services in Grid-based infrastructures.Through a service-based approach we can define integrated services for supporting distributed business intelligence tasks in Grids. Those services can address all the aspects that must be considered in data mining and in knowledge discovery processes such as data selection and transport, data analysis, knowledge models representation and visualization. We worked in this direction for providing Grid-based architectures and services for distributed knowledge discovery such as the Knowledge Grid the Weka4WS toolkit, and mobile Grid services for data mining.Here we describe a strategy and a model based on the use of services for the design of distributed knowledge discovery services and discuss how Grid frameworks, such those mentioned above, can be developed as a collection of services and how they can be used to develop distributed data analysis tasks and knowledge discovery processes using the SOA model.

How distributed data mining tasks can thrive as knowledge services

Data mining (DM) is increasingly used in the analysis of data generated in life sciences, including biological data produced in several disciplines such as genomics and proteomics, medical data produced in clinical practice, and administrative data produced in health care. The difficulty in mining such data is twofold. First of all, data in life sciences are inherently heterogeneous, spanning from molecular level data to clinical and administrative data. Second, data in life sciences are produced at an increasing rate and data repositories are becoming very large. Thus, the management and analysis of such data is becoming a main bottleneck in biomedical research. The main goal of this paper is to review the main methodologies to mine life sciences data and the ways they are coupled to high-performance infrastructures and systems that result in an efficient analysis. This paper recalls basic concepts of DM, grids, and distributed DM on grids, and reviews main approaches to mine biomedical data on high-performance infrastructures with special focus on the analysis of genomics, proteomics, and interactomics data, and the exploration of magnetic resonance images in neurosciences. The paper can be of interest both to bioinformaticians, who can learn how to exploit high performance infrastructures to mine life sciences data, and to computer scientists, who can address the heterogeneity and the high volumes of life sciences data at the data management, algorithm, and user interface layers. © 2013 Wiley Periodicals, Inc.

Data mining and life sciences applications on the grid

During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates.Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others.Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application.

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Business Intelligence and Performance Management

As modern data mining applications increase in complexity, so too do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale, and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.

M. Rohm

Papers

Digging Deep into the Data Mine with DataMiningGrid