How distributed data mining tasks can thrive as knowledge services

Abstract

Introduction Computer 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.

Data mining techniques and algorithms in cloud environment a review

“…Software as a Service: SaaS is a service which is provided to the consumer who can use the application form the cloud infrastructure. The consumer does not have any worry about how to manage or control the cloud infrastructure [5]. The Cloud is Virtualized data center which can manage or control itself.…”

“…Some feature vectors in an n-dimensional space can be used to represent the objects, where n means the total number of the features that are being used for the description of the clusters. [17] Once it is done, the algorithm will choose k-points in the vector space. These k-points become initial centers of the cluster.…”

Open data and distributed knowledge work in open government

“…From this perspective, web services can be understood as networked machine-to-machine communication, which is as a rule automated; its common language is based on open, manufacturer-independent standards (Zeppenfeld and Finger, 2009, p.38). Collections of services in grid frameworks based on service-oriented architecture (SOA) models are used for the design of distributed knowledge discovery processes (Talia and Trunfio, 2010). As a nexus of various applications, web services have two invariable requirements: a paradigm shift in the understanding of the role and function of software and in the handling of data, governance, and access rights.…”

Data Mining in Cloud Computing

“…Talia et al [13] summarize four levels of data mining services in cloud computing . (see Figure 3) Single KDD steps: the underlying composition data mining algorithms.…”