23,331 research outputs found
Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses
A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses
Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
The growing expanse of e-commerce and the widespread availability of online
databases raise many fears regarding loss of privacy and many statistical
challenges. Even with encryption and other nominal forms of protection for
individual databases, we still need to protect against the violation of privacy
through linkages across multiple databases. These issues parallel those that
have arisen and received some attention in the context of homeland security.
Following the events of September 11, 2001, there has been heightened attention
in the United States and elsewhere to the use of multiple government and
private databases for the identification of possible perpetrators of future
attacks, as well as an unprecedented expansion of federal government data
mining activities, many involving databases containing personal information. We
present an overview of some proposals that have surfaced for the search of
multiple databases which supposedly do not compromise possible pledges of
confidentiality to the individuals whose data are included. We also explore
their link to the related literature on privacy-preserving data mining. In
particular, we focus on the matching problem across databases and the concept
of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins
Digital Twins (DT) are essentially Dynamic Data-driven models that serve as
real-time symbiotic "virtual replicas" of real-world systems. DT can leverage
fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional
symbiotic sensing feedback loops for its continuous updates. Sensing loops can
consequently steer measurement, analysis and reconfiguration aimed at more
accurate modelling and analysis in DT. The reconfiguration decisions can be
autonomous or interactive, keeping human-in-the-loop. The trustworthiness of
these decisions can be hindered by inadequate explainability of the rationale,
and utility gained in implementing the decision for the given situation among
alternatives. Additionally, different decision-making algorithms and models
have varying complexity, quality and can result in different utility gained for
the model. The inadequacy of explainability can limit the extent to which
humans can evaluate the decisions, often leading to updates which are unfit for
the given situation, erroneous, compromising the overall accuracy of the model.
The novel contribution of this paper is an approach to harnessing
explainability in human-in-the-loop DDDAS and DT systems, leveraging
bidirectional symbiotic sensing feedback. The approach utilises interpretable
machine learning and goal modelling to explainability, and considers trade-off
analysis of utility gained. We use examples from smart warehousing to
demonstrate the approach.Comment: 10 pages, 1 figure, submitted to the 4th International Conference on
InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
The Role of Maintenance and Facility Management in Logistics: A Literature Review
Purpose - The purpose of this paper is to provide a literature review on the different ways of carrying out Facility Management and related topics in order to uncover that there is limited research regarding the impact of Facility Management on the logistics and operational performance of warehouses. Design/methodology/approach - Four different focus areas have been identified and for each one different methodologies and streams of research have been studied. Findings - The study underlines the importance of Facility Management for the logistics operations; therefore it supports the notion that investments aiming at preserving the status of the building and service components of warehouses are crucial. Originality/value - This paper aims to suggest to Facility Management managers that they can contribute to enhance business performance by designing effective Facility Management strategie
Analysing B2B electronic procurement benefits â Information systems perspective
This paper presents electronic procurement benefits identified in four case companies. The
benefits achieved in the case companies were classified according to taxonomies from the
Information Systems discipline. Existing taxonomies were combined into a new taxonomy
which allows evaluation of the complex e-procurement impact. Traditional financial-based
methods failed to capture the nature of e-procurement benefits. In the new taxonomy, eprocurement
benefits are classified using scorecard dimensions (strategic, tactical and
operational), which allows the identification of areas of e-procurement impact, in addition
the benefits characteristic is captured (tangible, intangible, financial and non-financial)
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
Driving continuous improvement
The quality of improvement depends on the quality of leading and lagging performance indicators. For this reason, several tools, such as process mapping, cause and effect analysis and FMEA, need to be used in an integrated way with performance measurement models, such as balanced scorecard, integrated performance measurement system, performance prism and so on. However, in our experience, this alone is not quite enough due to the amount of effort required to monitor performance indicators at operational levels. The authors find that IT support is key to the successful implementation of performance measurement-driven continuous improvement schemes
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