A Novel Approach Using Steganography and Cryptography in Business Intelligence

In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy.

K-Nearest Neighbors Algorithm (KNN)

The cryptocurrency is the first implementation of blockchain technology. This technology provides a set of tracks and innovation in scientific research, such as use of data either to detect anomalies either to predict price in the Bitcoin and the Ethereum. Furthermore, the blockchain technology provide a set of technique to automate the business process. This chapter presents a review of some research works related to cryptocurrency. A model with a KNN algorithm is proposed to detect illicit transaction. The proposed model uses both the elliptic dataset and KNN algorithm to detect illicit transaction. Furthermore, the elliptic dataset contains 203,769 nodes and 234,355 edges; it allows to classify the data into three classes: illicit, licit, or unknown. Each node has associated 166 features. The first 94 features represent local information about the transaction. The remaining 72 features are called aggregated features. The accuracy exceeded 90% with k=2 and k=4, the recall reaches 56% with k=3, and the precision reaches 78% with k=4.

Enterprise Data Lake Management in Business Intelligence and Analytics

The data lake has recently emerged as a scalable architecture for storing, integrating, and analyzing massive data volumes characterized by diverse data types, structures, and sources. While the data lake plays a key role in unifying business intelligence, analytics, and data mining in an enterprise, effective implementation of an enterprise-wide data lake for business intelligence and analytics integration is associated with a variety of practical challenges. In this chapter, concrete analytics projects of a globally industrial enterprise are used to identify existing practical challenges and drive requirements for enterprise data lakes. These requirements are compared with the extant literature on data lake technologies and management to identify research gaps in analytics practice. The comparison shows that there are five major research gaps: 1) unclear data modelling methods, 2) missing data lake reference architecture, 3) incomplete metadata management strategy, 4) incomplete data lake governance strategy, and 5) missing holistic implementation and integration strategy.

Big Data Quality for Data Mining in Business Intelligence Applications

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

From Business Intelligence to Big Data

Boundaries between business intelligence (BI), big data (BD), and big data analytics (BDA) are often unclear and ambiguous for companies. BD is a new research challenge; it is becoming a subject of growing importance. Notably, BD was one of the big buzzwords during the last decade. BDA can help executive managers to plan an organization's short-term and long-term goals. Furthermore, BI is considered as a kind of decision support system (DSS) that can help organizations achieving their goals, creating corporate value and improving organizational performance. This chapter provides a comprehensive view about the interrelationships between BI, BD, and BDA. Moreover, the chapter highlights the power of analytics that make them considered as one of the highly impact's organizational capability. Additionally, the chapter can help executive managers to decide the way to integrate BD initiatives as a tool, or as an industry, or as a corporate strategy transformation.

Integration of Data Mining and Business Intelligence in Big Data Analytics

The purpose of this chapter is to analyze and explore the research studies for scholarly publication trends and patterns related to the integration of data mining in particular business intelligence in big data analytics domains published in the period of 2010-2019. Research patterns explore in highly prestigious sources that have high impact factors and citations counted in the ISI Web of Science Core Collection database (indexes included SCI-Exp and SSCI). Bibliometric analysis methods applied for this study under the research limitations. Research questions formed based on bibliometric principles concentrating fields such as descriptive of publication, author productivity, country-regions distribution, keyword analysis with contribution among researchers, citation analysis, co-citation patterns searched. Findings showed strong relations and patterns on these important research domains. Besides this chapter would useful for researchers to obtain an overview of publication trends on research domains to be concerned for further studies and shows the potential gaps in those fields.

Modelling in Support of Decision Making in Business Intelligence

Modelling in support of decision making in business intelligence (BI) starts with exploring the BI systems, driven by artificial intelligence (AI). The purpose why AI will be the core of next-gen analytics and why BI will be empowered by it are determined. The role of AI and machine learning (ML) in business processes automation is analyzed. The benefits from AI integration in BI platforms are summarized. Then analysis goes through predictive modeling in the domain of e-commerce. The use of ML for predictive modeling is overviewed. Construction of predictive and clustering models is proposed. After that the importance of self-services in BI platforms is outlined. In this context the self-service BI is defined and what are the key steps to create successful self-service BI model are sketched. The effects of potential threads which are the results of the big data in the business world are examined and some suggestions for the future have been made. Lastly, game-changer trends in BI and future research directions are traced.

Causal Feature Selection

Feature selection is a process of the data preprocessing task in business intelligence (BI), analytics, and data mining that urges for new methods that can handle with high dimensionality. One alternative that have been researched to deal with the curse of dimensionality is causal feature selection. Causal feature selection is not based on correlation, but the causality relationship among variables. The main goal of this chapter is to present, based on the issues identified on other methods, a new strategy that considers attributes beyond those that compounds the Markov blanket of a node and calculate the causal effect to ensure the causality relationship.

Data Mining and Business Intelligence

From the middle of this second decade of the 21st century, analytics has become commonly associated with the topics business intelligence and data mining. Data mining (DM) is being applied with success in business intelligence (BI) environments and several examples of applications can be found. BI and DM have different roots and, as a consequence, have significantly different characteristics. DM came up from scientific environments; thus, it is not business oriented. DM tools still demand heavy work in order to obtain the intended results. On the contrary, BI is rooted in industry and business. As a result, BI tools are user-friendly. This chapter reflects on these differences from a historical perspective. Starting with a separated historical perspective of each one, analytics, BI, and DM, the author then discusses how they converged when DM is used and integrated in BI environments with success.

A Framework to Evaluate Big Data Fabric Tools

A huge growth in data and information needs has led organizations to search for the most appropriate data integration tools for different types of business. The management of a large dataset requires the exploitation of appropriate resources, new methods, as well as the possession of powerful technologies. That led the surge of numerous ideas, technologies, and tools offered by different suppliers. For this reason, it is important to understand the key factors that determine the need to invest in a big data project and then categorize these technologies to simplify the choice that best fits the context of their problem. The objective of this study is to create a model that will serve as a basis for evaluating the different alternatives and solutions capable of overcoming the major challenges of data integration. Finally, a brief analysis of three major data fabric solutions available on the market is also carried out, including Talend Data Fabric, IBM Infosphere, and Informatica Platform.