Data Mining Practical Machine Learning Tools and Techniques

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade‐off in efficiency, security, and functionality. Recent work proposes a file‐injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well‐chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the T F × I D F $TF\times IDF$ rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID‐19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient.

Forward privacy multikeyword ranked search over encrypted database

As international trade has been developing at an unprecedented rate, export product competitiveness is significant in a country's trade system. At present, neural network algorithms are extensively used in economic forecasting. Scholars have verified the effectiveness of neural networks, especially long short term memory (LSTM) model, compared with other nonlinear prediction methods. However, there are still research blanks in forecasting economic indicators related to export product competitiveness, and there is little research on the improved LSTM model combined with other algorithms. To address this limitation, this article established a quantitative index system for export product competitiveness composed of four highly recognized indicators such as revealed comparative advantage index, net export revealed comparative advantage index, international market share index, and economic value added. After collecting the relevant economic data, the single LSTM model was used to predict the future trend of China's machinery and equipment export product competitiveness. Based on the shortcomings exposed in applying single LSTM prediction, the article innovatively designs a combined forecast model of LSTM and seasonal autoregressive integrated moving average, and whose effectiveness and superiority are proved by the mean square error value. Finally, the article points out that this model plays an important role in national trade policy making. It is optimistic about the development of deep learning in international trade product competitiveness indicators prediction.

SALSTM: An improved LSTM algorithm for predicting the competitiveness of export products

Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection. 

A novel approach for the detection of anomalous energy consumption patterns in industrial cyber‐physical systems

Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.

https://www.mdpi.com/1424-8220/21/13/4322/pdf

Semantic Data Mining in Ubiquitous Sensing: A Survey.

Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

Chanmee Sirichanya

Papers

Semantic data mining in the information age: A systematic review