Applying Data Mining in Smart Home

Data mining aims to develop a new generation of tools to intelligently assist humans in analyzing mountains of data. Over the past few years, great progress has been made in both research and applications of data mining. Data mining systems have helped many businesses by exposing previously unknown patterns in their databases, which were used to improve profits, enhance customer services, and ultimately achieve a competitive advantage. In this paper, we present our unique data mining system DM-II (Data Mining Integration and Interestingness). DM-II is a PC-based system working in Windows 95/98/NT environment. Apart from the normal components of a data mining system, DM-II has a number of unique and advanced sub-systems. These sub-systems have been applied in many real-life applications, including education applications, insurance application, accident application, disease application, drug screening application, image classification, etc. Here, we focus on discussing the following sub-systems: CBA: CBA originally stands for Classification-Based on Associations [lo]. It has now been extended with a number of other advanced features. In all, CBA has the following capabilities: Building classifiers using association rules: Traditionally, association rule mining and classifier building are regarded as two different data mining tasks. CBA unifies the two tasks. It is able to ,generate association rules and builds a classifier using a special subset of the association rules. What is significant is that CBA, in general, produces more accurate classifiers compared to the state-ofthe-art classification system C4.5 [ 161. It also helps to solve some outstanding problems with the existing classification systems. Pruning and summarizing the discovered associations: One major problem with association

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

Background:Technological advances in the smart home have created new opportunities for supporting digital citizens’ well-being and facilitating their empowerment but have enabled new types of complex online harms to develop. Recent statistics have indicated that ‘smart’ technology ownership increases yearly, driven by lower costs and increased accessibility. Research on smart homes has also grown, focusing on technology perspectives at the expense of a user-centric approach sensitive to the smart home’s harms, risks, and vulnerabilities. Objective:This scoping review addresses the information gap by underscoring the scope of literature that exists regarding complex online harms, vulnerabilities, and risks associated with smart home technologies and citizens’ agency. The goal is to understand the state of knowledge, gaps in the literature, and areas for future study. The importance and originality of this paper lie in its interdisciplinary review and approach. It is hoped that this research will contribute to a deeper understanding of complex online harms in the smart home. Design:Three online databases were utilised to identify papers published between 2017 and 2022, from which we selected 235 publications written in English that addressed harms, risks, vulnerabilities, and agency in the smart home context. This allowed us to map contemporary literature to reveal significant gaps in our understanding of the complex online harms affecting smart home users and identify opportunities for further research. Results:This review identified emerging themes of ‘risks’, ‘vulnerabilities’, and ‘harms’ in that order of frequency within the literature on smart homes. The usage of terms is skewed towards computing science and information security, which comprised the majority of the literature at 54.6%. Human–computer interaction papers contributed 24.4%, while social sciences accounted for 16.2%. Conclusion:Risks, harms and vulnerabilities within smart home ecosystems and IoTs are ongoing issues with complexities that necessitate research. Privacy, security, and well-being are key themes that embody the scope of complex harms affecting smart home devices in the broad literature. This review establishes disciplinary research gaps, especially in user-centred perspectives, due to a heavy technology focus in the existing literature. Therefore, further research is needed to address emergent risks, harms and vulnerabilities of smart home devices and understand how user agency and autonomy can complement the design, interface, and socio-technical aspects of smart home systems.

Smart Home research started in 2000. Since then, Smart Home has attracted the attention of many researchers, and several studies have been conducted. This study aims to chart Smart Home research trends using bibliometric analyses from all years up to the publication date indexed in Scopus. We use keywords related to Smart Home in the Scopus database. Analysis techniques that use parameters include the year of publication, Scopus quartile range, year of publication, type of publication, journal publication, the publication of the most frequently cited maps, and density display mapping. The results show that the Q1 journal dominated since the first Smart Home publication in 2000 and peaked in 2013. The journal article type is the most commonly published compared to research conferences and other publications. Journal publications show the majority of IEEE transactions in consumer electronics. Three Smart Home publications have been cited more than 1563 times. This study also found that the words ‘smart home’, ‘technology,’ and ‘management’ are widely used by researchers. For future research, you should use keywords like ‘smart home energy management,’ ‘smart home environment,’ ‘sensor,’ ‘electric vehicle,’ and ‘smart home technology.’

In order to solve the problems of distribution network, equipment, management difficulty, low-real-time distribution network information, this paper systematically analyzes the current situation of distribution network operation and maintenance. As well as the application and research of data mining and analysis processing technology in distribution network, big data mining application is introduced into daily operation and maintenance of distribution network, and lean management is carried out to strengthen the effective control and control of power network from the source, so as to eliminate waste and create value according to ‘eliminate waste, create value, continuous improvement, excellence’ requirements, all.KeywordsResource allocationLean managementDetection mechanismArtificial intelligenceEnergy management

The healthcare big data industry is rapidly developing globally, and data mining and knowledge services in the healthcare field have become one of the core demands for its development. Data mining in healthcare is beneficial to improve the efficiency of diagnosis and treatment of patients, which is helpful to formulate more effective treatment plans and reduce medical costs. In this paper, we searched the core journals on China Knowledge Network and web of science by subject terms, and eliminated the irrelevant articles for literature counting. In this paper, the commonly used models and algorithms of data mining in healthcare are firstly elaborated; then the progress of the application of this technology in assisting medical tasks, optimizing resource allocation and improving health information services are respectively reviewed, summarizing the segmentation, classic algorithms and representative studies implied by each application. However, the application of data mining technology in healthcare also faces some problems, from data collection, to data cleaning, preprocessing, visualization, to the selection of algorithms and evaluation of results, each link is full of difficulties and challenges. Finally, this paper proposes future research directions such as diversifying data sources, strengthening security and privacy protection, developing visualization and analysis tools, accurately using big data to improve the service level of healthcare institutions, semanticizing electronic medical records mining, and improving cancer prevention. At the same time, data mining is deeply integrated with cloud computing, artificial intelligence and other fields to jointly promote scientific and technological progress in the field of health care.

With the rapid development of the Internet and communication technology, online education has drawn more and more attention, online learning platforms, on the other hand, store massive learner behavioral data and educational data. How to effectively analyze and utilize the data to improve the quality of online education has become a key issue urgently needed to be solved in the field of big data in education(BDE), educational data mining(EDM) is exactly an effective and practical method and means of applying BDE. Therefore, EDM is an important academic research hotspot in the field of EDM. Firstly, the paper introduces the basic concepts of BDE, EDM and online learning platform, and then elaborates on the process of how educational data mining transforms raw data into knowledge. Finally, the key technologies of data mining are classified according to their uses, and gives its application in online education scene. The paper can provide some guidance for the research and application of educational data mining based on online education.

BackgroundMulti-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.ResultsWe developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.ConclusionOur new method reduces the computational complexity from O(N3) to O(N) when the dimension of the feature space is far less than the number of genes N, and it successfully reconstructs the low dimensional representation as does the classical MDS. Its performance depends on the grouping method and the minimal number of the intersection points between groups. Feasible methods for grouping methods are suggested; each group must contain both neighboring and far apart data points. Our method can represent high dimensional large data set in a low dimensional space not only efficiently but also effectively.

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The data mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and data integrity. Every E-commerce and social website in World Wide Web uses the Classification is one of the data mining problems receiving great attention recently in the database community. Neural network is not suitable for data mining directly, because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. Different concise symbolic rules with high accuracy can be extracted from a neural network with the proposed approach. The neural network is first trained to achieve the required accuracy in data mining. In this paper we are going to combine neural network with the three different algorithms which are commonly used in data mining to improve the data mining result. These three algorithms are CHARM Algorithm, Top K Rules mining and CM SPAM Algorithm. The different datasets of online e-commerce website filpkart and Amazon are used to train the neural network and to use in data mining. The results of all three data mining algorithm with neural network techniques then tested on the available datasets and result are compared by computational complexity of the algorithm.

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.

Discovering interesting patterns in transactional databases is often a challenging area by the length of patterns and number of transactions in data mining, which is prohibitively expensive in both time and space. Closed itemset mining is introduced from traditional frequent pattern mining and having its own importance in data mining applications. Recently, regular itemset mining gained lot of attention in data mining research because of its occurrence behavior. In this paper we propose a new method called RCP-method (regular-closed pattern method) to mine regular-closed itemsets in transactional database by using vertical data format. Our RCP-method generates complete set of regular-closed patterns in transactional databases for a user given regularity threshold and support. Our experimental results show that this method is efficient in memory and execution time.

Noting that several rule discovery algorithms in data mining can produce a large number of irrelevant or obvious rules from data, there has been substantial research in data mining that addressed the issue of what makes rules truly 'interesting'. This resulted in the development of a number of interestingness measures and algorithms that find all interesting rules from data. However, these approaches have the drawback that many of the discovered rules, while supposed to be interesting by definition, may actually (1) be obvious in that they logically follow from other discovered rules or (2) be expected given some of the other discovered rules and some simple distributional assumptions. In this paper we argue that this is a paradox since rules that are supposed to be interesting, in reality are uninteresting for the above reason. We show that this paradox exists for various popular interestingness measures and present an abstract characterization of an approach to alleviate the paradox. We finally discuss existing work in data mining that addresses this issue and show how these approaches can be viewed with respect to the characterization presented here.

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.

This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.