The Paradigm Of Architecture And Optimization Of Integrating For Data Mining Algorithms And Multi Agent Systems With Business Intelligence

These days, organizations using DM are not a choice, since, everything is data, and it collects or generateseverywhere, in every activity or process that they are falling all over it, which hire data scientists anddynamic exploratory tools in comprehensive systems. Since, privacy advocates are concerned about thiswealthy handling, managing and reuse. Therefore, Scientists, data explorer, and users scramble to findnew ways to search, extract and change into a presumable object “that is information and knowledge."DM, BI and MAS for searching valuable information are well matured and in the modern businesses orother fields’ data/information and storage facilities, structures are no longer big issues. However, singleapproaches for tools and agent technologies could not be addressed business challenges, which theapplication tools and technologies as individual approaching of searching valuable information and accessor disseminations are problems that visualize how organizations are starving information. It is theemerging of an integrated and intelligent way of handling, managing, using the available data.Therefore, in this dissertation, we introduced and deeply discussed on Architectures and Optimization of aParadigm Integrating for DM and MAS with BI. It is a fundamental way of avoiding these challenges andsystematic tactics and scenarios, which is essential to optimize tools and agent’s applications andperformance in parallel to the growth of IT, business’s dynamisms, users’ desires and domain contextsthat led to the ultimate goal of business success. We applied different modeling architecture andalgorithms based on scientific theories and principles as the domain context and users need. Theapproaches are an IET or changes of the classic mining process into an integrated and intelligent systemto optimizing BI performance and application. The outcomes of the dissertation are developing a genericmodel, which are significant to describe physical, biological and social systems that can be a singleinference and crosscutting model. Besides to these, innovative and novel modeling and mining algorithmsand techniques are introduced, explored and evaluated.Furthermore, this dissertation has two fundamental interventions, such as Science and business. As asconce intervention, it is the approaches of change of classical mining approach into an integrated andintelligent system. Chapter3,5,6and7mainly focused on it. The details’ techniques and algorithms arecapable and scalable to involve various BI tools such as DW, OLAP, OLTP and others in dynamicarchitecture bases that allowing users to access and disseminate data/information as they need. It is amethodical and novel approach to optimize advanced tool’s applications and agents’ technologies toexplore the available large-scale data, which would be the possible way of optimization the modern BIsystems. The second or business intervention visualized as Integration of tool’s applications and agents’ technologies are the newly IET, which requires for decision makers, researchers and users as an effectivemeans to enhance their businesses “soft power” and added value for the reconstruction and revolution ofthe traditional business process. These are mainly discussed on chapter8,9and10, whereas chapter4both data science as the business prospects and chapter11domain based exploration of the proposedapproaches. Therefore, without integrating for tool’s applications and agents’ technologies will not end upcomplete and promising conclusions into consideration of efficiency and effectiveness to be solutions ofbusiness challenges.Integrating for DM and MAS are powerful visualization of large and complex data sets, the kinds ofinformation that would be readily apparent about business or organization as a whole, which provides anaccess of ubiquitous computing, any data, accessing and disseminating that made fetching to improve theBPs of BPM systems. Since it is the issues or demand of efficiency and effectiveness of BP throughintegration and intelligent approach’s problem solving, performance optimization and risk mitigation,which the fundamental tasks and methodologies of modern business that help to describe real-worldmatters.“It is the transformation from information to intelligent ages.” It is a paradigm and scientificapproaches of integrating for applications and agent’s technologies, which are capable, dynamic andadaptable, various issues and fields. The prominent contribution of this dissertation is summarized asfollows.-Single approaches of tackling business challenges did not effective in many reasons. In thisresearch, we introduced and discussed a novel integration for tools and agents techniques andmethodologies, which gives insight a best adaptation of business scanning system to the specificneeds of the various businesses that allowing decision-makers to optimum decision outcomes,-Graphic representation of challenges and its appropriate solutions, which is vitally significant andeasy to conceptualize the business context. The modern architecture of a generic BI model andmining algorithms are required for mining complex or large-scale data and describes the variousrelevant aspects of BP that help for easing in BI systems and tools to maximize user’s capability.-This research outcome vitally significant to narrow the communication gap between decision-makers and experts by providing them a common reference framework, concepts and vocabularyto be accurate identify and describe or access and analyze the various aspects,This dissertation is organized into three major categories, which include the (i) research foundation arts oftechnologies, and requirement analysis that discussed in chapter1,2and3.(ii) Models and algorithmsdevelopments and proposed innovative approaches, which discussed in chapter4,5,6and7.(iii) Modeling exploration, discussion and performance measurements, including applications under chapter8,9,10and ii, and final conclusion and research direction in chapter12which followed by the cited list ofreferences.The dissertation, specifically chapter6-11is supported by12scientific papers, which are6published (2EI indexed journals,2international conference and EI and ISTP indexed, and2by the known publisherData mining Book chapters). The other6(one is accepted, and5under review) in different status andtime. Besides to these, the research work supported by9subject matter courses work papers among these5papers were accepted for international conference.