A uniform parallel optimization method for data mining grid

This paper addresses the task allocation problem in open, dynamic grid or service-oriented environments. In such environments, both grid/service providers and consumers can be modeled as intelligent agents. These agents can leave off and enter into an open environment freely at any time. Task allocation under time constraints becomes a critical issue in such environments since it is difficult to apply a central controller during the allocation process due to the openness of environments as well as decentralized natures of agents. This paper proposes a negotiation-based method for task allocation under time constraints in an open, dynamic grid environment, where both consumer and provider agents can enter into or leave off the environment freely. In this method, there is no central controller, and agents negotiate with each other for task allocation based only on local views. The experimental results show that the proposed method can outperform state-of-art methods in terms of success rate of task allocation and total profit obtained from the allocated tasks by agents under different time constraints.

Java has become a language of choice for applications executing in heterogeneous environments utilising distributed objects and multithreading. To handle large data sets, scalable and efficient implementations of data mining approaches are required, generally employing computationally intensive algorithms. Conventional Java implementations do not directly provide support for the data structures often encountered in such algorithms, and they also lack repeatability in numerical precision across platforms. This paper describes a distributed framework employing task and data parallelism, and implemented in high performance Java (HPJava). Issues of interest for data mining algorithms are identified, and possible solutions discussed for overcoming limitations in the Java Virtual Machine. The framework supports parallelism across workstation clusters, using the message-passing interface as middleware, and can support different analysis algorithms, wrapped as Java objects, and linked to various databases using the Java database connectivity interface. Guidelines are provided for implementing parallel and distributed data mining on large data sets, and a proof-of-concept data mining application is analysed using a neural network.

A significant amount of attention appears to be focusing on how to better collect, analyze, and disseminate information. In doing so, technology is commonly and increasingly looked upon as both a tool, and, in some cases, a substitute, for human resources. One such technology that is playing a prominent role in homeland security initiatives is data mining. Similar to the concept of homeland security, while data mining is widely mentioned in a growing number of bills, laws, reports, and other policy documents, an agreed upon definition or conceptualization of data mining appears to be generally lacking within the policy community (Relyea, 2002). While data mining initiatives are usually purported to provide insightful, carefully constructed analysis, at various times data mining itself is alternatively described as a technology, a process, and/or a productivity tool. In other words, data mining, or factual data analysis, or predictive analytics, as it also is sometimes referred to, means different things to different people. Regardless of which definition one prefers, a common theme is the ability to collect and combine, virtually if not physically, multiple data sources, for the purposes of analyzing the actions of individuals. In other words, there is an implicit belief in the power of information, suggesting a continuing trend in the growth of “dataveillance,” or the monitoring and collection of the data trails left by a person’s activities (Clarke, 1988). More importantly, it is clear that there are high expectations for data mining, or factual data analysis, being an effective tool. Data mining is not a new technology but its use is growing significantly in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, enhance research, and increase sales. In the public sector, data mining applications initially were used as a means to detect fraud and waste, but have grown to also be used for purposes such as measuring and improving program performance. While not completely without controversy, these types of data mining applications have gained greater acceptance. However, some national defense/homeland security data mining applications represent a significant expansion in the quantity and scope of data to be analyzed. Moreover, due to their security-related nature, the details of these initiatives (e.g., data sources, analytical techniques, access and retention practices, etc.) are usually less transparent.

Data mining has become a major academic research area over the last ten years. However, in the transition from academic prototypes to commercial products there have been few successes with commercial data mining applications failing to make any significant impact in the marketplace. In this paper it is argued that most data mining applications concentrate on the model-building phase of the data mining process and rarely engage the user in other stages. The paper reviews the challenges of producing successful data mining applications and in particular the role of information visualisation. Visualisation in data mining tends to be used to present final results, rather than playing an important part throughout the entire process. We argue that an immersive environment may provide the user with a more suitable interface than is commonly offered. We present a virtual data mining environment which attempts to integrate a data mining application interface and information visualisation in a seamless manner, using the concept of 'liquid data'.

Experiences about job migration on a dynamic grid environment

Multi-Channel Transmission Control for Data Mining in Wireless Sensor Networks

Knowledge management is an application that includes human, processes and technology for managing information and knowledge resources in an organization. With its variety of sophisticated techniques, data mining appears within the knowledge management process of knowledge creation in particular. Organizations make use of knowledge management application and data mining in multiple functional aspects such as manufacturing, finance and marketing. With respect to marketing, organizational products, marketing decisions are very critical to their profit generation. This paper reviews the application of knowledge management and data mining in marketing decisions. The objective of the study is to infer the application of knowledge management and data mining in marketing decision making. Using the scoping review technique, a number of 30 scholarly articles on knowledge management, data mining and marketing decision have been gathered and reviewed. The analysis of the retrieved articles found that clustering was the most frequent technique applied in marketing decision. It is also identified that sale and cash registers are marketing dimensions regularly reported to apply data mining. Scholars have also acknowledged that marketing decisions have effectively applied data mining.

With the requirement to find methods to analysis the rapidly increasing data, as a branch of computer science, data mining technique has been widely apply in different domain .One of the most enticing application areas of data mining in these emerging technologies is in finance. To complete the financial tasks, data mining technique is used refer to Stock market forecasting, Bankruptcy Prediction, Credit Risk Estimation, Corporate Performance Prediction and Management Fraud. This paper focuses on new data mining applications in finance. The fast development of Compute technology (such as Cloud Computing) and database together gives data mining a new challenge and chance in data mining of finance. How to apply the new computer and database technique to the data mining, and integrate the previous framework and the new technology together to build a more efficiency and safer framework is a long way to go in the future . Meanwhile, after accumulate enough empirical regularity and experiences, deep field-specific theories will be set up. This means, in the future, it will have more standardized and accurate model and method to apply in different domain of finance by generating more empirical regularities and combining them with domain knowledge.

Five high-quality workshops were held at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) in Bangkok, Thailand during April 27-30, 2009. There were 17, 6, 9, 4 and 5 accepted papers to be presented at the Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2009), the workshop on Advances and Issues in Biomedical Data Mining (AIBDM 2009), the workshop on Data Mining with Imbalanced Classes and Error Cost (ICEC 2009), the workshop on Open Source in Data Mining (OSDM 2009), and the workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2009). One competition, PAKDD 2009 Data Mining Competition, and one local workshop, Thai Track Session, were arranged. From these workshops (except PAISI which published its works in separate LNCS proceedings), we selected two or three best papers for this LNCS publication. PAKDD is a major international conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scientific discovery,data visualization, causal induction and knowledge-based systems.

Data mining and machine learning (ML) methods are used more than ever in cyber security. The use of machine learning (ML) is one of the potential solutions that may be successful against zero-day attacks, starting with categorising IP traffic and filtering harmful traffic for intrusion detection. In this field, certain published systematic reviews were taken into consideration. Recent systematic reviews may incorporate older and more recent works in the topic of investigation.. Both security professionals and hackers use data mining capabilities. Applications for data mining may be used to analyze programme activity, surfing patterns, and other factors to identify potential cyber-attacks in the future. The new study uses statistical traffic features, ML, and data mining approaches. This research performs a concentrated literature review on machine learning and its usage in cyber analytics for email filtering, traffic categorization, and intrusion detection. Each approach was identified, and a summary was provided based on the relevancy and quantity of citations. Some well-known datasets are also discussed since they are a crucial component of ML techniques. On when to utilize a certain algorithm is also offered some advice. Four ML algorithms have been evaluated on MODBUS data gathered from a gas pipeline. Using ML algorithms, other assaults have been categorized, and then the effectiveness of each approach has been evaluated. This study demonstrates the use of ML and data mining for threat research and detection, focusing on malware detection with high accuracy and short detection times.

Data mining (DM) is a powerful information technology (IT) tool in today’s competitive business world, especially as our human society entered the Big Data era. From academic point of view, it is an area of the intersection of human intervention, machine learning, mathematical modeling and databases. In recent years, data mining applications have become an important business strategy for most companies that want to attract new customers and retain existing ones. Using mathematical techniques, such as, neural networks, decision trees, mathematical programming, fuzzy logic and statistics, data mining software can help the company discover previously unknown, valid, and actionable information from various and large sources (either databases or open data sources like internet) for crucial business decisions. The algorithms of the mathematical models are implemented through some sort of computer languages, such as C++, JAVA, structured query language (SQL), on-line analysis processing (OLAP) and R. The process of data mining can be categorized as selecting, transforming, mining, and interpreting data. The ultimate goal of doing data mining is to find knowledge from data to support user’s decision. Therefore, data mining is strongly related with knowledge and knowledge management.

Data mining and scoring tool providers require users to use provider-specific ways to invoke their services. The provider-specific approach could be a major factor affecting why data mining tools and applications are not currently as widespread as one might hope. However, SOA can implements the loose coupling between the services provider and services consumer. Using Web service protocol (such as SOAP, WSDL and UDDI), the data mining task can be encapsulated into a standard Web services. Services consumer can dynamically bound different services provider based on Web services, which implements the integration of data mining applications with data mining and scoring tool. The Java Data Mining API (JDM) is the first attempt to create a standard Java API to access data-mining tools from Java applications. A new service discovery model where quality of service is taken as constraints when searching for Web services would give some confidence to the Web service consumers about the quality of the service they are about to invoke.

In software development, the software requirements are the result of understandings of user requirements or the system owners to the developer. Based on these requirements, the developers are able to develop their software architecture. Based on software architecture, developers can construct a set of requirements. This reuse process shall give positive impacts to a better software product. This research uses a technique for generating software requirements for data mining based on data mining software architecture, especially a data mining application for analyzing social media. The contribution of this research is a proposal of a generic data mining requirements software that is expected to be reference for data mining application development. The proposal is evaluated to three different data mining applications that each consists of four typical requirements. These three applications are compared to our generated requirements. The results show that 50% of our requirements have been used in these applications or an average of 83.33% requirements from our proposal has contributed to these three applications. It shows that the technique used to generate software requirements based on software architecture is able to provide an alternative to develop new common requirements.

In today's business organization, the use of technology become more wider and data mining is one of the method in order to make business processes run smoothly. Data mining is the exploration and analysis of large quantities of data in order to discover valid, potentially useful, and ultimately understandable patterns in data. However, there are also challenges or limitations that prevent the use of data mining application in organization. The purpose of this literature and article review is to identify challenges occur when applying data mining in knowledge management towards organization. This study is significance to individual as well as organization in order to aware the challenges when applying data mining so that the works in organization become more effective and productive.

In knowledge management process, data mining technique can be used to extract and discover the valuable and meaningful knowledge from a large amount of data. Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010; Park, 2001; Sinha, 2008; Tso & Yau, 2007; Wan, 2009; Zanakis, 2005; Zhuang et al., 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others. Classification and prediction tasks are among the popular tasks in data mining; and widely used in many areas especially for trend analysis and future planning. In fact, classification technique is supervised learning, which is the class level or prediction target is already known. As a result, the classification model which is represented through rules structures will be constructed in the classification process. In this case, the constructed model will be representing the precious knowledge and it can be used for future planning. There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008; Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction. In addition prediction applications are mainly developed in business and industrious fields; and quite restricted studies involved human talent in an organization (Jantan et al., 2009). HR data can provide a rich resource for knowledge discovery and for decision support system development. Recently, an organization has to struggle effectively in term of cost, quality, service or innovation. All these depend on having enough right people with the right skills, employed