60,371 research outputs found
Experiments with Data Mining in Enterprise Management
Abstract This paper describes experiments in applying data mining techniques to historical data collected by network monitoring agents. Large amounts of performance data, including network, system, and application performance data, are collected and stored by monitoring agents. Data mining algorithms analyze the data and codify it into usable knowledge. We show, via experiments, that the knowledge contains useful and unexpected suggestions for improving the effectiveness of business processes and for reducing management support effort. Four experiments are discussed: three preliminary lab experiments and one large, real-world experiment at a major airline company
A case study of predicting banking customers behaviour by using data mining
Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model
Ontology-based knowledge representation of experiment metadata in biological data mining
According to the PubMed resource from the U.S. National Library of Medicine,
over 750,000 scientific articles have been published in the ~5000 biomedical journals
worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes
Mining Event Logs to Support Workflow Resource Allocation
Workflow technology is widely used to facilitate the business process in
enterprise information systems (EIS), and it has the potential to reduce design
time, enhance product quality and decrease product cost. However, significant
limitations still exist: as an important task in the context of workflow, many
present resource allocation operations are still performed manually, which are
time-consuming. This paper presents a data mining approach to address the
resource allocation problem (RAP) and improve the productivity of workflow
resource management. Specifically, an Apriori-like algorithm is used to find
the frequent patterns from the event log, and association rules are generated
according to predefined resource allocation constraints. Subsequently, a
correlation measure named lift is utilized to annotate the negatively
correlated resource allocation rules for resource reservation. Finally, the
rules are ranked using the confidence measures as resource allocation rules.
Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and
the presented approach, and the results show that the presented approach is
effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource
allocation, Knowl. Based Syst. (2012), http://dx.doi.org/
10.1016/j.knosys.2012.05.01
Organizational Chart Inference
Nowadays, to facilitate the communication and cooperation among employees, a
new family of online social networks has been adopted in many companies, which
are called the "enterprise social networks" (ESNs). ESNs can provide employees
with various professional services to help them deal with daily work issues.
Meanwhile, employees in companies are usually organized into different
hierarchies according to the relative ranks of their positions. The company
internal management structure can be outlined with the organizational chart
visually, which is normally confidential to the public out of the privacy and
security concerns. In this paper, we want to study the IOC (Inference of
Organizational Chart) problem to identify company internal organizational chart
based on the heterogeneous online ESN launched in it. IOC is very challenging
to address as, to guarantee smooth operations, the internal organizational
charts of companies need to meet certain structural requirements (about its
depth and width). To solve the IOC problem, a novel unsupervised method Create
(ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1)
social stratification of ESN users into different social classes, (2)
supervision link inference from managers to subordinates, and (3) consecutive
social classes matching to prune the redundant supervision links. Extensive
experiments conducted on real-world online ESN dataset demonstrate that Create
can perform very well in addressing the IOC problem.Comment: 10 pages, 9 figures, 1 table. The paper is accepted by KDD 201
- …