This first of a kind book places spatial data within the broader domain of information technology (IT) while providing a comprehensive and coherent explanation of the guiding principles, methods, ...implementation and operational management of spatial databases within the workplace. The text explains the key concepts, issues and processes of spatial data implementation and provides a holistic management perspective that complements the technical aspects of spatial data stressed in other textbooks. In this respect, this book is unique in its coverage of spatial database principles and architecture, database modelling including UML, database and spatial data standards, spatial data infrastructure, database implementation, and workplace-oriented project management including user needs study and end user education. The text first overviews the current state of spatial information technology and it concludes with a speculative account of likely future developments. Cutting edge research and practical workplace needs are defined and explained. Topics covered, among others, include strategies for end user education, current spatial data standards and their importance, legal issues and liabilities in the ownership and use of spatial data, spatial metadata use within distributed databases, the Internet and Web-based solutions to database deployment, quality assurance and quality control in database implementation and use, spatial decision support, and spatial data mining. The book applies equally to senior undergraduate and graduate courses and students, as well as spatial data managers and practitioners already in the workplace. It will enhance their technical and human-resource based understanding of spatial data management. Certification courses that seek to prepare students for careers in the spatial information industry and courses targeted at enhancing needed geospatial workplace knowledge and skills will benefit greatly from its content.
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This textbook addresses students, professionals, lecturers and researchers interested in software product line engineering. With more than 100 examples and about 150 illustrations, the authors ...describe in detail the essential foundations, principles and techniques of software product line engineering.
The authors are professionals and researchers who significantly influenced the software product line engineering paradigm and successfully applied software product line engineering principles in industry. They have structured this textbook around a comprehensive product line framework.
Software product line engineering has proven to be the paradigm for developing a diversity of software products and software-intensive systems in shorter time, at lower cost, and with higher quality. It facilitates platform-based development and mass customisation. The authors elaborate on the two key principles behind software product line engineering: (1) the separation of software development in two distinct processes, domain and application engineering; (2) the explicit definition and management of the variability of the product line across all development artefacts.
As a student, you will find a detailed description of the key processes, their activities and underlying techniques for defining and managing software product line artefacts. As a researcher or lecturer, you will find a comprehensive discussion of the state of the art organised around the comprehensive framework. As a professional, you will find guidelines for introducing this paradigm in your company and an overview of industrial experiences with software product line engineering.
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Software product lines represent perhaps the most exciting paradigm shift in software development since the advent of high-level programming languages. Nowhere else in software engineering have we ...seen such breathtaking improvements in cost, quality, time to market, and developer productivity, often registering in the order-of-magnitude range. While the underlying concepts are straightforward enough building a family of related products or systems by planned and careful reuse of a base of generalized software development assets the devil can be in the details, as successful product line practice can involve organizational change, business process change, and technology change. The authors ideally combine academic research results with industrial real-world experiences, thus presenting a broad view on product line engineering so that both managers and technical specialists will benefit from reading it. After presenting a common framework for the description of the industrial case studies, they capture the wealth of knowledge that eight companies have gathered during the introduction of the software product line engineering approach in their daily practice. After reading this book, you will understand all the relevant aspects, regarding business, architecture, process, and organizational issues, of applying software product line engineering. If you consider using a product line approach in your organization, or if you want to improve your current practices you will find a rich set of useful information at your fingertips from practitioners to practitioners.
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Information Systems Applications (incl.Internet); Business Information Systems; Computer Appl. in Administrative Data Processing; Management of Computing and Information Systems
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This comprehensive book outlines and explains the parameters of design science research. It demonstrates how to conduct such research, and provides examples of various types of research that have ...been conducted using design science.
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Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues ...and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side information. We propose MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation. MKR is a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task. The two tasks are associated by crosscompress units, which automatically share latent features and learn high-order interactions between items in recommender systems and entities in the knowledge graph. We prove that crosscompress units have sufficient capability of polynomial approximation, and show that MKR is a generalized framework over several representative methods of recommender systems and multi-task learning. Through extensive experiments on real-world datasets, we demonstrate that MKR achieves substantial gains in movie, book, music, and news recommendation, over state-of-the-art baselines. MKR is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.
•PLS-PM has been subject to many improvements in last years.•Prior PLS guidelines have not covered the entire recent developments.•We explain how to perform and report an up-to-date empirical ...analysis with PLS.•We provide a fictive illustrative example on business value of social media.
Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.
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With the ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large ...datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information, while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named Knowledge Graph Enhanced Contextualized Attention-Based Network (KGCAN). It explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing KG-based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.
The business value of investments in information technology/information system (IT/IS) has been the subject of active research over several decades. Even though a plethora of similar studies ...analyzing the impact of promised IT/IS investments on firm performance exists, the results, largely inconclusive, mostly concentrate on the developed countries. In this backdrop, and with an expected manifold rise in IT/IS investments in India in the coming years, an assessment of the relationship between investments and firm performance can be noteworthy. The study explores this important issue by analyzing the impact of IT/IS investments on the firm's performance in India based on data of around 6500 IT/IS investments during 2000-2016. We deploy a series of univariate and multivariate analyses and complement those with several robustness tests. Our principal findings indicate that IT/IS investments on the average in India have been mostly unsuccessful in impacting firm performance positively, in line with "productivity paradox" phenomenon previously documented in the U.S. and other markets. We substantiate our principal results using several robustness tests. We offer several possible explanations of our results spanning across both IS as well as finance literature and discuss the implications of future investment prospects for firms. The results highlight the need for adoption of caution by firms operating in emerging economies like India while considering future IT/IS investment decisions. These suggestions are likely to serve as a good reference point in other emerging economies as well.
Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely ...practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.
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