Generating requirements from systems models using patterns: a case study

Academic research has produced many model-based specification and analysis techniques, however, most organisations continue to document requirements as textual statements. To help bridge this gap between academic research and requirements practice, this paper reports an extension to the RESCUE process in which patterns for generating requirements statements from i* system models were manually applied to i* models developed for a complex air traffic control system. The paper reports the results of this application and describes them with examples, the benefits of the approach to the project, and ongoing research to implement these patterns in the REDEPEND modelling tool to make requirements engineers more productive. We review similar work on requirements modelling and expression, and compare our work to it to demonstrate the proposed advance in the state of the art. Finally the paper discusses future uses of requirements generation from model patterns in RESCUE.     

Designing embedded systems using patterns: A case study

If software for embedded processors is based on a time-triggered architecture, using co-operative task scheduling, the resulting system can have very predictable behaviour. Such a system characteristic is highly desirable in many applications, including (but not restricted to) those with safety-related or safety-critical functions. In practice, a time-triggered, co-operatively scheduled (TTCS) architecture is less widely employed than might be expected, not least because care must be taken during the design and implementation of such systems if the theoretically predicted behaviour is to be obtained. In this paper, we argue that the use of appropriate ‘design patterns’ can greatly simplify the process of creating TTCS systems. We briefly explain the origins of design patterns. We then illustrate how an appropriate set of patterns can be used to facilitate the development of a non-trivial embedded system.     

Mining user access patterns with traversal constraint for predicting web page requests

The recent increase in HyperText Transfer Protocol (HTTP) traffic on the World Wide Web (WWW) has generated an enormous amount of log records on Web server databases. Applying Web mining techniques on these server log records can discover potentially useful patterns and reveal user access behaviors on the Web site. In this paper, we propose a new approach for mining user access patterns for predicting Web page requests, which consists of two steps. First, the Minimum Reaching Distance (MRD) algorithm is applied to find the distances between the Web pages. Second, the association rule mining technique is applied to form a set of predictive rules, and the MRD information is used to prune the results from the association rule mining process. Experimental results from a real Web data set show that our approach improved the performance over the existing Markov-model approach in precision, recall, and the reduction of user browsing time. Mei-Ling Shyu received her Ph.D. degree from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN in 1999, and three Master's degrees from Computer Science, Electrical Engineering, and Restaurant, Hotel, Institutional, and Tourism Management from Purdue University. She has been an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Miami (UM), Coral Gables, FL, since June 2005, Prior to that, she was an Assistant Professor in ECE at UM dating from January 2000. Her research interests include data mining, multimedia database systems, multimedia networking, database systems, and security. She has authored and co-authored more than 120 technical papers published in various prestigious journals, refereed conference/symposium/workshop proceedings, and book chapters. She is/was the guest editor of several journal special issues. Choochart Haruechaiyasak received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Miami, in 2003 with the Outstanding Departmental Graduating Student award from the College of Engineering. After receiving his degree, he has joined the National Electronics and Computer Technology Center (NECTEC), located in Thailand Science Park, as a researcher in Information Research and Development Division (RDI). His current research interests include data/ text/ Web mining, Natural Language Processing, Information Retrieval, Search Engines, and Recommender Systems. He is currently leading a small group of researchers and programmer to develop an open-source search engine for Thai language. One of his objectives is to promote the use of data mining technology and other advanced applications in Information Technology in Thailand. He is also a visiting lecturer for Data Mining, Artificial Intelligence and Decision Support Systems courses in many universities in Thailand. Shu-Ching Chen received his Ph.D. from the School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA in December, 1998. He also received Master's degrees in Computer Science, Electrical Engineering, and Civil Engineering from Purdue University. He has been an Associate Professor in the School of Computing and Information Sciences (SCIS), Florida International University (FIU) since August, 2004. Prior to that, he was an Assistant Professor in SCIS at FIU dating from August, 1999. His main research interests include distributed multimedia database systems and multimedia data mining. Dr. Chen has authored and co-authored more than 140 research papers in journals, refereed conference/symposium/workshop proceedings, and book chapters. In 2005, he was awarded the IEEE Systems, Man, and Cybernetics Society's Outstanding Contribution Award. He was also awarded a University Outstanding Faculty Research Award from FIU in 2004, Outstanding Faculty Service Award from SCIS in 2004 and Outstanding Faculty Research Award from SCIS in 2002.     

Extracting user web browsing patterns from non-content network traces: The online advertising case study

Online advertising is a rapidly growing industry currently dominated by the search engine ’giant’ Google. In an attempt to tap into this huge market, Internet Service Providers (ISPs) started deploying deep packet inspection techniques to track and collect user browsing behavior. However, these providers have the fear that such techniques violate wiretap laws that explicitly prevent intercepting the contents of communication without gaining consent from consumers. In this paper, we explore how it is possible for ISPs to extract user browsing patterns without inspecting contents of communication.Our contributions are threefold. First, we develop a methodology and implement a system that is capable of extracting web browsing features from stored non-content based network traces, which could be legally shared. When such browsing features are correlated with information collected by independently crawling the Web, it becomes possible to recover the actual web pages accessed by clients. Second, we evaluate our system on the Internet and check that it can successfully recover user browsing patterns with high accuracy.     

Generating page clippings from web search results using a dynamically terminated genetic algorithm

We present a page clipping synthesis (PCS) search method to extract relevant paragraphs from other web search results. The PCS search method applies a dynamically terminated genetic algorithm to generate a set of best-of-run page clippings in a controlled amount of time. These page clippings provide users the information they are most interested in and therefore save the users time and trouble in browsing lots of hyperlinks. We justify that the dynamically terminated genetic algorithm yields cost-effective solutions compared with solutions reached by conventional genetic algorithms. Meanwhile, effectiveness measure confirmed that PCS performs better than general search engines.     

Modeling the web as a hypergraph to compute page reputation

In this work we propose a model to represent the web as a directed hypergraph (instead of a graph), where links connect pairs of disjointed sets of pages. The web hypergraph is derived from the web graph by dividing the set of pages into non-overlapping blocks and using the links between pages of distinct blocks to create hyperarcs. A hyperarc connects a block of pages to a single page, in order to provide more reliable information for link analysis. We use the hypergraph model to create the hypergraph versions of the Pagerank and Indegree algorithms, referred to as HyperPagerank and HyperIndegree, respectively. The hypergraph is derived from the web graph by grouping pages by two different partition criteria: grouping together the pages that belong to the same web host or to the same web domain. We compared the original page-based algorithms with the host-based and domain-based versions of the algorithms, considering a combination of the page reputation, the textual content of the pages and the anchor text. Experimental results using three distinct web collections show that the HyperPagerank and HyperIndegree algorithms may yield better results than the original graph versions of the Pagerank and Indegree algorithms. We also show that the hypergraph versions of the algorithms were slightly less affected by noise links and spamming.     

A note on the paper: Optimizing web servers using page rank prefetching for clustered accesses

In this short note, we briefly present and discuss an example of page rank algorithm given in [Information Sciences 150 (2003) 165–176].     

Automated analysis of timed security: a case study on web privacy

This paper presents a case study on an automated analysis of real-time security models. The case study on a web system (originally proposed by Felten and Schneider) is presented that shows a timing attack on the privacy of browser users. Three different approaches are followed: LH-Timed Automata (analyzed using the model checker HyTech), finite-state automata (analyzed using the model checker NuSMV), and process algebras (analyzed using the model checker CWB-NC ). A comparative analysis of these three approaches is given.     

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.     

Universal web accessibility and the challenge to integrate informal Arabic users: a case study

Universal Access in the Information Society - Most Arabs can read text written in Modern Standard Arabic (MSA). However, to easily express themselves, they may find it easier to switch to informal...     

Adaptive coordination in cardiac anaesthesia: a study of situational changes in coordination patterns using a new observation system

Patient care in hospital settings requires coordinated team performance. Studies in other industries show that successful teams adapt their coordination processes to the situational task requirements. This prospective field study aimed to test a new observation system and investigate patterns of adaptive coordination within operating room teams. A trained observer recorded coordination activities during 24 cardiac surgery procedures. The study tested whether different patterns occur during different phases of and between different types of surgical procedures (two-way multivariate ANOVA with repeated measure). A statistically significant increase was found in clinical and coordination activities in phases of the operation with high task interdependence. The highest level of 'coordination via the work environment' (i.e. an implicit coordination mechanism) was recorded during the actual procedure on the beating heart. These findings prove the sensitivity of the observation system developed and evaluated in this study and provide insight into patterns of adaptive coordination in cardiac anaesthesia. This study furthers our understanding of adaptive coordination as a cornerstone of effective team performance in complex work environments. Using a new observation system, it describes patterns employed by health care professionals in response to changing task demands in an acute patient care setting.     

A web personalizing technique using adaptive data structures: The case of bursts in web visits

The explosive growth in the size and use of the World Wide Web continuously creates new great challenges and needs. The need for predicting the users’ preferences in order to expedite and improve the browsing though a site can be achieved through personalizing of the Websites. Recommendation and personalization algorithms aim at suggesting WebPages to users based on their current visit and past users’ navigational patterns. The problem that we address is the case where few WebPages become very popular for short periods of time and are accessed very frequently in a limited temporal space. Our aim is to deal with these bursts of visits and suggest these highly accessed pages to the future users that have common interests. Hence, in this paper, we propose a new web personalization technique, based on advanced data structures.The data structures that are used are the Splay tree (1) and Binary heaps (2). We describe the architecture of the technique, analyze the time and space complexity and prove its performance. In addition, we compare both theoretically and experimentally the proposed technique to another approach to verify its efficiency. Our solution achieves O(P²) space complexity and runs in k log P time, where k is the number of pages and P the number of categories of WebPages.     

A simplistic model for identifying prominent web users in directed multiplex social networks: a case study using Twitter networks

This paper aims to describe a new simplistic model dedicated to gauge the online influence of Twitter users based on a mixture of structural and interactional features. The model is an additive mathematical formulation which involves two main parts. The first part serves to measure the influence of the Twitter user on just his neighbourhood covering his followers. However, the second part evaluates the potential influence of the Twitter user beyond the circle of his followers. Particularly, it measures the likelihood that the tweets of the Twitter user will spread further within the social graph through the retweeting process. The model is tested on a data set involving four kinds of real-world egocentric networks. The empirical results reveal that an active ordinary user is more prominent than a non-active celebrity one. A simple comparison is conducted between the proposed model and two existing simplistic approaches. The results show that our model generates the most realistic influence scores due to its dealing with both explicit (structural and interactional) and implicit features.     

DOMtegrity: ensuring web page integrity against malicious browser extensions

In this paper, we address an unsolved problem in the real world: how to ensure the integrity of the web content in a browser in the presence of malicious browser extensions? The problem of exposing confidential user credentials to malicious extensions has been widely understood, which has prompted major banks to deploy two-factor authentication. However, the importance of the “integrity” of the web content has received little attention. We implement two attacks on real-world online banking websites and show that ignoring the “integrity” of the web content can fundamentally defeat two-factor solutions. To address this problem, we propose a cryptographic protocol called DOMtegrity to ensure the end-to-end integrity of the DOM structure of a web page from delivering at a web server to the rendering of the page in the user’s browser. DOMtegrity is the first solution that protects DOM integrity without modifying the browser architecture or requiring extra hardware. It works by exploiting subtle yet important differences between browser extensions and in-line JavaScript code. We show how DOMtegrity prevents the earlier attacks and a whole range of man-in-the-browser attacks. We conduct extensive experiments on more than 14,000 real-world extensions to evaluate the effectiveness of DOMtegrity.