Business intelligence approach to supporting strategy-making of ISP service management

The recent deregulation of telecommunication industry by the Taiwanese government has brought about the acute competition for Internet Service Providers (ISP). Taiwan’s ISP industry is characterized by the heavy pressure for raising revenue after hefty capital investments of last decade and the lack of knowledge to develop competitive strategies. To attract subscribers, all ISP dealers are making an all-out effort to improve their service management. This study proposes a Business Intelligence process for ISP dealers in Taiwan to assist management in developing effective service management strategies. We explore the customers’ usage characteristics and preference knowledge through applying the attribute-oriented induction (AOI) method on IP traffic data of users. Using the self-organizing map (SOM) method, we are able to divide customers into clusters with different usage behavior patterns. We then apply RFM modeling to calibrate customers’ value of each cluster, which will enable the management to develop direct and effective marketing strategies. For network resource management, this research mines the facility utilization over various administrative districts of the region, which could assist management in planning for effective network facilities investment. With actual data from one major ISP, we develop a BI decision support system with visual presentation, which is well received by its management staff.     

Enabling customer relationship management in ISP services through mining usage patterns

The monopoly of state ownership of telecommunication industry in Taiwan was lifted in 1997. In choosing an ISP, pricing was and still is a main differentiating factor in the mind of customers; however, service quality has emerged as a major concern among users lately. Management of ISP has discovered that service quality is important not only for attracting new customers, but, more importantly, for retaining existing customers who may otherwise be lured away by lower fees. Hence, it is essential to develop a CRM system, which could help keeping existing customers and exploring further business opportunities at the same time. In this study, we, based on the IP traffic data, developed a CRM systematic approach for a major ISP company in Taiwan to enhance customers' longer-term loyalty. This approach employs CRISP-DM methodology, and applies Attribute-Oriented Induction as the mining technique to discover network usage behaviors of customers, which help management identify usage pattern and also pinpoint the time when usage is excessively heavy. The former allows management to make effective personal calls for services or maintenance, and the latter presents opportunities for management to offer personalized cares and advanced products. Pixel-oriented visualization is applied to improve the understanding of mining results.     

Exploiting and transferring presentational knowledge assets in R&D organizations

Nowadays, how to exploit and transfer the value of knowledge assets effectively has been the primary challenge faced by global R&D organizations. Surprisingly, there seems to be little argument about managing presentational knowledge assets, which are widely used by knowledge workers to present their ideas, proposals, findings and reports. This paper develops the management model and related IT enabling tools that support users to better exploit and transfer presentational knowledge assets. The solution integrates text extractor, slideshow generator, knowledge repository, content-based retrieval and ontology-enabled search engine along with the goal of portraying the search results in a visual navigation form. The performance satisfaction of the proposed system was proved statistically by conducting the user’s survey of effectiveness and usability. This solution has demonstrated to be a feasible way for better managing contents, prompting cognitive learning, improving presentation production and presentational knowledge transformation, and consequently facilitates the value leverage of the presentational knowledge assets.     

Adaptive QoS Control Based on Benefit Optimization for Video Servers Providing Differentiated Services

We propose and analyze quality of service (QoS) control algorithms for video servers designed to provide differentiated video streaming services. The design concepts are based on resource reservation and benefit optimization so that resources are reserved dynamically and adaptively for different QoS levels in response to the changing workload of the system, with the objective of maximizing the benefit throughput obtainable by the system. We analyze the benefit throughput obtainable by the system for a baseline algorithm for which the QoS levels of admitted users are not changed during the service lifetime and a greedy algorithm that may raise QoS levels of admitted users due to resources being free from departure events. We validate the design of these two QoS control algorithms via a detailed simulation study.     

Constructing tree-based knowledge structures from text corpus

A knowledge structure identifies how people think and displays a macro view of human perception. By discovering the hidden structural relations of knowledge, significant reasoning patterns are retrieved to enhance further knowledge sharing and distribution. However, the utilization of such approaches is apt to be limited due to the lack of hierarchical features and the problem of information overload, which make it difficult to enhance comprehension and provide effective navigation. To address these critical issues, we propose a new approach to construct a tree-based knowledge structure from corpus which can reveal the significant relations among knowledge objects and enhance user comprehension. The effectiveness of the proposed method is demonstrated with two representative public data sets. The evaluation results show that the method presented in this work achieves remarkable consistency with the domain-specific knowledge structure, and is capable of reflecting appropriate similarities among knowledge objects along with hierarchical implications in the document classification task.     

A frame knowledge system for managing financial decision knowledge

Managing decision knowledge or expertise from domain experts is one of the most exciting challenges in today’s knowledge management field. The nature of decision knowledge in determining a firm’s financial health is context-dependent, intangible, and tacit in nature. Knowledge-based systems (KBS) have been recognized as a successful paradigm for managing financial decision knowledge attributed to possessing capabilities of reasoning and enhancing the consistency of decision-making. However, most present KBS adopt rules as the knowledge representation scheme, which cannot express the expert’s knowledge construct systematically when dealing with more numerous and disordered knowledge connotations. In addition, the standalone nature of the systems hinders them from deploying onto heterogeneous platforms and cannot accommodate to the emerging Web-enabled environment. To reduce these flaws, this study proposes a frame knowledge system in which the structural and procedural decision knowledge is encapsulated so that unnecessary interference can be avoided. A protocol analysis, before encapsulation, is conducted to elicit the tacit and unstructured knowledge from a senior CPA we cooperated with. The deployment and Web enabling issue is tackled by using Jess and Java interoperable computing. With these combined, it is possible to prompt the understandability, accessibility, and reusability of KBS. The effectiveness of the proposed system is validated in supporting the expert’s decision-making by conducting an empirical experimentation on 537 companies listed in the Taiwan Stock Exchange Corporation.     

Predicting financial activity with evolutionary fuzzy case-based reasoning

Predicting financial activity through examining the short-term liquidity is crucial within today’s turbulent financial environment. Firms, governments, and individuals all need an effective methodology based on liquidity information that plays performance deterioration warning a priori bankruptcy prediction. In this paper, we propose a hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate. GAs are used to determine the optimal or near-optimal weight vector of financial features expressed in linguistic values by the expert. A fuzzy k-NN-based CBR scheme is designed to compute memberships of financial activity rates and to provide a more flexible and practical mechanism for acquiring, creating, and reusing the expert’s decision knowledge. An empirical experimentation using 746 publicly traded Taiwanese firms shows that the average accuracy of the rating is about 92.36%, which is superior to other related models. The proposed approach not only can lend support to the decision of an expert, but also allow proper feedback for the expert to improve the quality of the decision.     

Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles

Locating helpful reviewers in opinion-sharing communities is an important issue. Numerous studies that examine this using social relations have some shortcomings. This study investigates language use, differing from person to person, and develops a novel prediction model to alleviate the problems. We identify four stylistic aspects and explore their impacts on predicting reviewers’ helpfulness ratings. The analyses show that the proposed model can more accurately locate helpful reviewers than the baseline model. In addition, reviewers’ words impact more than social relations do, although a combination of these will boost prediction performance to a greater extent than one alone.     

AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL

The study of fuzzy time series has attracted great interest and is expected to expand rapidly. Various forecasting models including high-order models have been proposed to improve forecasting accuracy or reducing computational cost. However, there exist two important issues, namely, rule redundancy and high-order redundancy that have not yet been investigated. This article proposes a novel forecasting model to tackle such issues. It overcomes the major hurdle of determining the k-order in high-order models and is enhanced to allow the handling of multi-factor forecasting problems by removing the overhead of deriving all fuzzy logic relationships beforehand. Two novel performance evaluation metrics are also formally derived for comparing performances of related forecasting models. Experimental results demonstrate that the proposed forecasting model outperforms the existing models in efficiency.     

A FCM-based deterministic forecasting model for fuzzy time series

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such a deficiency was recently solved by Li and Cheng [S.-T. Li, Y.-C. Cheng, Deterministic Fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications 53 (2007) 1904–1920] using a deterministic forecasting method. In this paper, we propose a novel forecasting model to enhance forecasting functionality and allow processing of two-factor forecasting problems. In addition, this model applies fuzzy c-means (FCM) clustering to deal with interval partitioning, which takes the nature of data points into account and produces unequal-sized intervals. Furthermore, in order to cope with the randomness of initially assigned membership degrees of FCM clustering, Monte Carlo simulations are used to justify the reliability of the proposed model. The superior accuracy of the proposed model is demonstrated by experiments comparing it to other existing models using real-world empirical data.