Jing Gao

Xi Chen

Gaikar Vilas

This is an open access book. 2023 3rd International Conference on Enterprise Management and Economic Development (ICEMED2023) will be held in Xi'an, China on May 12–14, 2023. Enterprise management is the general term for a series of functions such as organizing, planning, commanding, supervising and regulating the production and operation activities of enterprises. Relative to economic growth, economic development is the core concept of development economics. Economic development refers to the high-quality development of the economy, including quality and quantity, rather than merely the growth of quantity. Enterprise management covers economics, management, business management, financial management, human resource management and other aspects, and is a comprehensive interdisciplinary science that spans natural science, engineering science, technical science and humanities and social science. Enterprise management comes into being with the development of modern socialized mass production. The use of modern management means and methods to manage enterprises, ensure the survival and development of enterprises, and play a positive role in promoting economic development. ICEMED2023 will bring together experts and scholars from relevant fields to discuss the relationship between enterprise management and economic development. Reasonable enterprise management is an important way to promote the economic development of enterprises. Scientific and reasonable use of industrial and commercial enterprise management knowledge can reasonably carry out effective macro-control on the enterprise economy and ensure the stable progress and development of the enterprise economy.

Proceedings of the 2023 3rd International Conference on Enterprise Management and Economic Development (ICEMED 2023)

Andy Koronios

Sasa Baskarada

Contemporary organizations are producing and storing more information then ever before in history. The resulting information overload, combined with the lack of quality assurance for information management, has led to a questionable state of information quality in many organizations. Furthermore, assessing, enhancing, and managing information quality has proven to be a notoriously difficult undertaking. This paper presents a capability maturity model approach for information quality management process assessment and improvement. The paper first presents a set of criteria, as identified from extensive literature review and exploratory case studies, which are thought to be of importance when considering a holistic approach for information quality management. The paper then presents the results of a Delphi study, which was used to validate those criteria and organize them into a staged capability maturity model - Information Quality Management Capability Maturity Model (IQM-CMM). The paper finally presents the preliminary results of a case study, where IQM-CMM was applied in a large Australian engineering asset management organization, and used as a comprehensive approach for evaluating their existing information quality management practices. As a result, it is believed that IQM-CMM may help organizations in assessing their existing information quality management practices, and in identifying potential gaps and improvement strategies.

A Framework for Assessing Organizational Information Quality Management Capability Maturity

IQM-CMM

Jiawei Han

Manish Gupta

Charu Aggarwal

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers.

Outlier Detection for Temporal Data

Jing Gao

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