2,911 research outputs found
Datamining for Web-Enabled Electronic Business Applications
Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business
Data Mining for Web-Enabled Electronic Business Applications
Web-enabled electronic business is generating massive amounts of data on customer purchases, browsing patterns, usage times, and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for Web-enabled electronicbusiness. Copyright Idea Group Inc
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY
This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
From sequential patterns to concurrent branch patterns: a new post sequential patterns mining approach
A thesis submitted for the degree of Doctor ofPhilosophy
of the University of BedfordshireSequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences.
A novel model for sequential patterns called Sequential Patterns Graph (SPG) is proposed. The construction algorithm of SPG is presented with experimental results to substantiate the concept. The thesis then sets out to define some new structural patterns such as concurrent branch patterns, exclusive patterns and iterative patterns which are generally hidden behind sequential patterns. Finally, an integrative framework, named Post Sequential Patterns Mining (PSPM), which is based on sequential patterns mining, is also proposed for the discovery and visualisation of structural patterns.
This thesis is intended to prove that discrete sequential patterns derived from traditional sequential patterns mining can be modelled graphically using SPG. It is concluded from experiments and theoretical studies that SPG is not only a minimal representation of sequential patterns mining, but it also represents the interrelation among patterns and establishes further the foundation for mining structural knowledge (i.e. concurrent branch patterns, exclusive patterns and iterative patterns). from experiments conducted on both synthetic and real datasets, it is shown that Concurrent Branch Patterns (CBP) mining is an effective and efficient mining algorithm suitable for concurrent branch patterns
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From Classification Rules to Action Recommendations
Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when the number of rules is large. Ideally, the user would ultimately like to use these rules to decide which actions to take. In the literature, this notion is usually referred to as actionability. The contribution of this paper1 is two-fold: first we propose a survey of the main approaches developed to address actionability. This topic has received growing attention in the past years. We present a classification of the main research in this area as well as a comparative study between the different approaches. Second, we propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted with an "unsatisfactory situation" and needs help to decide what appropriate actions to take in order to remedy the situation. The method consists in comparing the situation to a set of classification rules. This is achieved by using a suitable distance that allows one to suggest action recommendations requiring minimal changes to improve the situation. We propose the algorithm DAKAR for learning action recommendations and we present an application to environment protection. Our experiment shows the usefulness of our contribution for action recommendation but also raises some concerns about the impact of the redundancy of a set of rules in learning action recommendations of good quality
Managing the consistency of distributed documents
Many businesses produce documents as part of their daily activities: software engineers
produce requirements specifications, design models, source code, build scripts and more;
business analysts produce glossaries, use cases, organisation charts, and domain ontology
models; service providers and retailers produce catalogues, customer data, purchase orders,
invoices and web pages.
What these examples have in common is that the content of documents is often semantically
related: source code should be consistent with the design model, a domain ontology
may refer to employees in an organisation chart, and invoices to customers should be consistent
with stored customer data and purchase orders. As businesses grow and documents
are added, it becomes difficult to manually track and check the increasingly complex relationships
between documents. The problem is compounded by current trends towards
distributed working, either over the Internet or over a global corporate network in large
organisations. This adds complexity as related information is not only scattered over
a number of documents, but the documents themselves are distributed across multiple
physical locations.
This thesis addresses the problem of managing the consistency of distributed and possibly
heterogeneous documents. āDocumentsā is used here as an abstract term, and does not
necessarily refer to a human readable textual representation. We use the word to stand
for a file or data source holding structured information, like a database table, or some
source of semi-structured information, like a file of comma-separated values or a document
represented in a hypertext markup language like XML [Bray et al., 2000]. Document
heterogeneity comes into play when data with similar semantics is represented in different
ways: for example, a design model may store a class as a rectangle in a diagram whereas
a source code file will embed it as a textual string; and an invoice may contain an invoice
identifier that is composed of a customer name and date, both of which may be recorded
and managed separately.
Consistency management in this setting encompasses a number of steps. Firstly, checks
must be executed in order to determine the consistency status of documents. Documents
are inconsistent if their internal elements hold values that do not meet the properties
expected in the application domain or if there are conflicts between the values of elements
in multiple documents. The results of a consistency check have to be accumulated and
reported back to the user. And finally, the user may choose to change the documents to
bring them into a consistent state.
The current generation of tools and techniques is not always sufficiently equipped to deal
with this problem. Consistency checking is mostly tightly integrated or hardcoded into tools, leading to problems with extensibility with respect to new types of documents.
Many tools do not support checks of distributed data, insisting instead on accumulating
everything in a centralized repository. This may not always be possible, due to organisational
or time constraints, and can represent excessive overhead if the only purpose of
integration is to improve data consistency rather than deriving any additional benefit.
This thesis investigates the theoretical background and practical support necessary to
support consistency management of distributed documents. It makes a number of contributions
to the state of the art, and the overall approach is validated in significant case
studies that provide evidence of its practicality and usefulness
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