コンピュータ・サイエンス : ACM computing surveys

Context: In recent years, many usability evaluation methods (UEMs) have been employed to evaluate Web applications. However, many of these applications still do not meet most customers' usability expectations and many companies have folded as a result of not considering Web usability issues. No studies currently exist with regard to either the use of usability evaluation methods for the Web or the benefits they bring. Objective: The objective of this paper is to summarize the current knowledge that is available as regards the usability evaluation methods (UEMs) that have been employed to evaluate Web applications over the last 14years. Method: A systematic mapping study was performed to assess the UEMs that have been used by researchers to evaluate Web applications and their relation to the Web development process. Systematic mapping studies are useful for categorizing and summarizing the existing information concerning a research question in an unbiased manner. Results: The results show that around 39% of the papers reviewed reported the use of evaluation methods that had been specifically crafted for the Web. The results also show that the type of method most widely used was that of User Testing. The results identify several research gaps, such as the fact that around 90% of the studies applied evaluations during the implementation phase of the Web application development, which is the most costly phase in which to perform changes. A list of the UEMs that were found is also provided in order to guide novice usability practitioners. Conclusions: From an initial set of 2703 papers, a total of 206 research papers were selected for the mapping study. The results obtained allowed us to reach conclusions concerning the state-of-the-art of UEMs for evaluating Web applications. This allowed us to identify several research gaps, which subsequently provided us with a framework in which new research activities can be more appropriately positioned, and from which useful information for novice usability practitioners can be extracted.

Usability evaluation methods for the web: A systematic mapping study

An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

System for monitoring and managing datacenters

How can a single person understand what’s going on in a collection of millions of documents? This is an increasingly widespread problem: sifting through an organization’s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field’s research. This monograph explores the ways that humans and computers make sense of document collections through tools called topic models. Topic models are a statistical framework that help users understand large document collections; not just to find individual documents but to understand the general themes present in the collection. Applications of Topic Models describes the recent academic and industrial applications of topic models. In addition to topic models’ effective application to traditional problems like information retrieval, visualization, statistical inference, multilingual modeling, and linguistic understanding, Applications of Topic Models also reviews topic models’ ability to unlock large text collections for qualitative analysis. It reviews their successful use by researchers to help understand fiction, non-fiction, scientific publications, and political texts. Applications of Topic Models is aimed at the reader with some knowledge of document processing, basic understanding of some probability, and interested in many application domains. It discusses the information needs of each application area, and how those specific needs affect models, curation procedures, and interpretations. By the end of the monograph, it is hoped that readers will be excited enough to attempt to embark on building their own topic models. It should also be of interest to topic model experts as the coverage of diverse applications may expose models and approaches they had not seen before.

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Applications of Topic Models

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an "open learning" model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.

/pdf/analyzing-customer-experience-feedback-using-text-mining-a-4e3bkat2m7.pdf

Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach

The invention provides a novel method and apparatus for analyzing a communication network such as a computer network or a social network comprising nodes communicating with each other by means of messages. A topic discovery unit is provided for performing a topic discovery on the basis of a message content which can be formed by textual content. A segmentation unit performs a segmentation of a global network graph representing the communication network into topic sub-graphs depending on the discovered topics. A property calculation unit calculates intra-topic network properties and inter-topic network properties of the network nodes. An advantage is that a deeper understanding of the analyzed communication network can be achieved such as communications patterns to identify roles of participating nodes. The method and apparatus according to the present invention can be used to optimize the organization of any communication network.

Method and an apparatus for analyzing a communication network

The rapid growth of the Internet has pushed the research and development of web usage mining ever more into focus. Web usage mining and its applications have become critical to the business world. These analyses rest in turn on the ability to develop a clear understanding of the actions a user has taken. So far, the temporal order of clicks has been taken to be equal to the structural order of a session. With the advent of the newest browser generation where the use of multiple tabs has become a common feature, the above assumption does not necessarily hold any more. It is crucial to understand how the use of multiple tabs impacts on web usage mining, especially on the understanding of a session and its reconstruction. In order to analyze this new browsing behavior, we introduce a generic browsing model extending the traditional serial or single window model to cover the use of multiple tabs. Based on this model, we present and analyze an approach to detect use of multiple tabs within sessions. The existence and increasing prominence of the use of multiple tabs is shown by this approach to be of relevance to business analysis as well as research results.

Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining

For the evaluation of Web sites, a multitude of metrics are available. Apart from general statistical measures, success metrics reflect the degree to which a Web site achieves its defined objectives. Particularly metrics for e-commerce sites based on transaction analysis are commonly available and well understood. In contrast to transaction based sites, the success of Web sites geared toward information delivery is harder to quantify since there is no direct feedback of user intent. User feedback is only directly available on transactional Web sites. We introduce a metric to measure the success of an information driven Web site in meeting its objective to deliver the desired information in a timely and usable fashion. We propose to assign a value to each click based on the type of transition, duration and semantic distance. These values are then combined into a scoring model describing the success of a Web site in meeting its objectives. The resulting metric is introduced as the GPI and its applicability shown on a large corporate Web site.

Guidance Performance Indicator " Web Metrics for Information Driven Web Sites

For companies acting on a global scale, the necessity to monitor and analyze news channels and consumer-generated media on the Web, such as weblogs and n news-groups, is steadily increasing. In particular the identification of novel trends and upcoming issues, as well as their dynamic evolution over time, is of utter importance to corporate communications and market analysts. Automated machine learning systems using clustering techniques have only partially succeeded in addressing these newly arising requirements, failing in their endeavor to properly assign short-term hype topics to long-term trends. We propose an approach which allows to monitor news wire on different levels of temporal granularity, extracting key-phrases that reflect short-term topics as well as longer-term trends by means of statistical language modelling. Moreover, our approach allows for assigning those windows of smaller scope to those of longer intervals.

Tracking Topic Evolution in News Environments

We propose an approach for exploring large corpora of textual customer feedback in a guided fashion, bringing order to massive amounts of unstructured information. The prototypical system we implemented allows an analyst to assess labelled clusters in a graphical fashion, based on treemaps, and perform drill-down operations to investigate the topic of interest in a more fine-grained manner. Labels are chosen by simple but effective term weighting schemes and lay the foundations for assigning feedback postings to clusters. In order to allow for drill-down operations leading to new clusters of refined information, we present an approach that contrasts foreground and background models of feedback texts when stepping into the currently selected set of feedback messages. The prototype we present is already in use at various Siemens units and has been embraced by marketing analysts.

/pdf/mining-and-exploring-unstructured-customer-feedback-data-4w8jqcrn6z.pdf

Maximilian Viermetz

Papers

Method and an apparatus for analyzing a communication network

Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining

Guidance Performance Indicator " Web Metrics for Information Driven Web Sites

Tracking Topic Evolution in News Environments

Mining and Exploring Unstructured Customer Feedback Data Using Language Models and Treemap Visualizations