Improving the Effectiveness of a Web Site with Web Usage Mining

Spiliopoulou, Myra; Pohle, Carsten; Faulstich, Lukas

doi:10.1007/3-540-44934-5_9

Cited by 67 publications

(36 citation statements)

References 8 publications

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“…They described a preliminary requirement investigation which was used as an adaptive wizard to test user's reaction to mock-ups of the ADSS with and without system advices. Spiliopoulou et al [20] considered three aspects of using web site usage data for marketing analysis:…”

Section: Related Modelsmentioning

confidence: 99%

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An Agent-based Decision Model for Electronic Payment System

Vincent¹

2012

IJCA

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Customers agent sent into the network for shopping are always faced with some complex choices of products, prices and brands. This paper presents a supporting scheme to solve the problems faced by agent in decision making in purchasing online products. Our model consist consists of negotiating semantic for customer and merchant agent with possible advise in accordance to the user preferences and motivations. Investigations are made prior to the user's reactions to responses from the customer agent. A provision of decision trees indication merchant and customers view are also incorporated in the model. The results of the risk analysis with expected response shows that transaction risk reduces with increase in expected response.

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Section: Related Modelsmentioning

confidence: 99%

“…Data acquisition can be non-invasive through recording user behavior without their interaction. Web server logs are the primary means of obtaining this information, which is often stored in web data warehouses for deep analysis [20].…”

Section: Related Modelsmentioning

confidence: 99%

An Agent-based Decision Model for Electronic Payment System

Vincent¹

2012

IJCA

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“…Several methods from different fields such as statistics, data mining, machine learning, and pattern recognition can be used to do pattern discovery. Kosala and Blockeel [106] classify the purpose of Web usage mining to either understanding the user behavior so that the Web site can be optimized accordingly [107], [108], [109] or to improve the website's effectiveness [110]. Web personalization to user preferences has been done using artificial intelligence techniques in [108], [109].…”

Section: User Profiling and Usage Pattern Discoverymentioning

confidence: 99%

Capacity metric for chip heterogeneous multiprocessors

Otoom

Paul

2011

Proceedings of the Seventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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The primary contribution of this thesis is the development of a new performance metric, Capacity, which evaluates the performance of Chip Heterogeneous Multiprocessors (CHMs) that process multiple heterogeneous channels. Performance metrics are required in order to evaluate any system, including computer systems. A lack of appropriate metrics can lead to ambiguous or incorrect results, something discovered while developing the secondary contribution of this thesis, that of workload modes for CHMsor Workload Specific Processors (WSPs).For many decades, computer architects and designers have focused on techniques that reduce latency and increase throughput. The change in modern computer systems built around CHMs that process multi-channel communications in the service of single users calls this focus into question. Modern computer systems are expected to integrate tens to hundreds of processor cores onto single chips, often used in the service of single users, potentially as a way to access the Internet. Here, the design goal is to integrate as much functionality as possible during a given time window. Without the ability to correctly identify optimal designs, not only will the best performing designs not be found, but resources will be wasted and there will be a lack of insight to what leads to better performing designs. To address performance evaluation challenges of the next generation of computer systems, such as multicore computers inside of cell phones, we found that a structurally different metric is needed and proceeded to develop such a metric.In contrast to single-valued metrics, Capacity is a surface with dimensionality related to the number of input streams, or channels, processed by the CHM. We develop some fundamental Capacity curves in two dimensions and show how Capacity shapes reveal interaction of not only programs and data, but the interaction of multiple data streams as they compete for access to resources on a CHM as well. For the analysis of Capacity surface shapes, we propose the development of a demand characterization method in which its output is in the form of a surface. By overlaying demand surfaces over Capacity iii surfaces, we are able to identify when a system meets its demands and by how much.Using the Capacity metric, computer performance optimization is evaluated against workloads in the service of individual users instead of individual applications, aggregate applications, or parallel applications. Because throughput was originally derived by drawing analogies between processor design and pipelines in the automobile industry, we introduce our Capacity metric for CHMs by drawing an analogy to automobile production, signifying that Capacity is the successor to throughput. By developing our Capacity metric, we illustrate how and why different processor organizations cannot be understood as being better performers without both magnitude and shape analysis in contrast to other metrics, such as throughput, that consider only magnitude.In this work, we make the following major ...

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“…Recently, the application of data mining techniques to the Web logs, referred to as Web Log Mining, has draw extensively attention from researchers from various disciplines [CSM97]. From Web logs, one can identify user access patterns which can be used for various purposes such as Web site design [SPF99], page recommendation [PPR96,DH99], Web caching and prefetching [PM96,PM98,SKS98,PP99,YZL01].…”

Section: Introductionmentioning

confidence: 99%

Cut-and-Pick Transactions for Proxy Log Mining

Lou

Liu

Lü

et al. 2002

Advances in Database Technology — EDBT 2002

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Abstract. Web logs collected by proxy servers, referred to as proxy logs or proxy traces, contain information about Web document accesses by many users against many Web sites. This "many-to-many" characteristic poses a challenge to Web log mining techniques due to the difficulty in identifying individual access transactions. This is because in a proxy log, user transactions are not clearly bounded and are sometimes interleaved with each other as well as with noise. Most previous work has used simplistic measures such as a fixed time interval as a determination method for the transaction boundaries, and has not addressed the problem of interleaving and noisy transactions. In this paper, we show that this simplistic view can lead to poor performance in building models to predict future access patterns. We present a more advanced cut-and-pick method for determining the access transactions from proxy logs, by deciding on more reasonable transaction boundaries and by removing noisy accesses. Our method takes advantage of the user behavior that in most transactions, the same user typically visits multiple, related Web sites that form clusters. These clusters can be discovered by our algorithm based on the connectivity among Web sites. By using real-world proxy logs, we experimentally show that this cut-and-pick method can produce more accurate transactions that result in Web-access prediction models with higher accuracy.

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Improving the Effectiveness of a Web Site with Web Usage Mining

Cited by 67 publications

References 8 publications

An Agent-based Decision Model for Electronic Payment System

An Agent-based Decision Model for Electronic Payment System

Capacity metric for chip heterogeneous multiprocessors

Cut-and-Pick Transactions for Proxy Log Mining

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