Mobile applications consume device energy for their operations, and the fast rate of battery depletion on mobile devices poses a major usability hurdle. After the display, data communication is the second-biggest consumer of mobile device energy. At the same time, software applications that run on mobile devices represent a fast-growing product segment. Typically, these applications serve as front-end display mechanisms, which fetch data from remote servers and display the information to the user in an appropriate format—incurring significant data communication overheads in the process. In this work, we propose methods to reduce energy overheads in mobile devices due to data communication by leveraging data caching technology. A review of existing caching mechanisms revealed that they are primarily designed for optimizing response time performance and cannot be easily ported to mobile devices for energy savings. Further, architectural differences between traditional client-server and mobile communications infrastructures make the use of existing caching technologies unsuitable in mobile devices. In this article, we propose a set of two new caching approaches specifically designed with the constraints of mobile devices in mind: (a) a response caching approach and (b) an object caching approach. Our experiments show that, even for a small cache size of 250MB, object caching can reduce energy consumption on average by 45% compared to the no-cache case, and response caching can reduce energy consumption by 20% compared to the no-cache case. The benefits increase with larger cache sizes. These results demonstrate the efficacy of our proposed method and raise the possibility of significantly extending mobile device battery life.

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase‐level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two‐thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self‐formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2‐second response times at the highest loads tested).

Organizations deliver on their mandates by executing a variety of services. Over the past few decades, service automation software systems, such as SAP and PeopleSoft, have enabled the automation of services. While much attention in the literature and in industry has been devoted to the implementation and functional correctness of automated services, little focus has been granted to ensuring responsiveness for services. As service automation platforms host larger and larger numbers of services, and services execute with greater and greater levels of concurrency, fault resolution becomes an important issue in ensuring expected responsiveness levels. In particular, two factors impact fault resolution in service automation platforms. First, each executing service requires access to specific data and system resources to complete its processing. As greater numbers of services execute concurrently, there is increasing contention for these data and system resources, leading to greater numbers of faults and SLA violations in service execution. Second, the black-box nature of service automation platforms provides little visibility into the nature of resource contention that caused a fault or SLA violation. This lack of visibility makes fault resolution difficult, and in many cases impossible, because it is difficult to trace the root cause of the problem. In this paper, the authors address the problem of system-level resource visibility for services through the design and development of a system capable of mapping abstract service workflows to their data and system impacts to enable resource visibility. The authors' system has been tested and demonstrated effective, as we demonstrate in a case study setting.

In this paper, we describe the design and implementation of a reverse dictionary. Unlike a traditional forward dictionary, which maps from words to their definitions, a reverse dictionary takes a user input phrase describing the desired concept, and returns a set of candidate words that satisfy the input phrase. This work has significant application not only for the general public, particularly those who work closely with words, but also in the general field of conceptual search. We present a set of algorithms and the results of a set of experiments showing the retrieval accuracy of our methods and the runtime response time performance of our implementation. Our experimental results show that our approach can provide significant improvements in performance scale without sacrificing the quality of the result. Our experiments comparing the quality of our approach to that of currently available reverse dictionaries show that of our approach can provide significantly higher quality over either of the other currently available implementations.

The article evaluates the feasibility of extending agile principles to larger, dynamic, and possibly distributed software development projects by uncovering the theoretical basis for agile values and principles for achieving agility. The extant literature focuses mainly on one theory – complex adaptive systems – to support agile methods, although recent research indicates that the control theory and the adaptive structuration theory are also applicable. This article proposes that at least three other theories exist that are highly relevant: transaction cost economics, social exchange theory, and expectancy theory. By employing these theories, a rigorous analysis of the Agile Manifesto is conducted. Certain agile values and principles find theoretical support and can be applied to enhance agility dynamic projects regardless of size; some agile principles find no theoretical support while others find limited support. Based on the analysis and the ensuing discussion, the authors propose a framework with five dimensions of agility: process, design, people, outcomes, and adaptation.

The Unified Modeling Language has been shown to be complex and difficult to learn. The difficulty of learning to build the individual diagrams in the UML, however, has received scant attention. In this article, we consider the case of the UML sequence diagram. Despite the fact that these diagrams are among the most frequently used in practice, they are difficult to learn to build. In this article, we consider the question of why these diagrams remain so difficult to learn to build. Specifically, we analyze the process of learning to build sequence diagrams in the context of cognitive complexity theory. Based on this analysis, and drawing on the theory of learner-centered design, we develop a set of recommendations for presenting the sequence diagram building task to the student analyst to reduce the complexity of learning how to build them.

Modern Web-based application infrastructures are based on clustered multitiered architectures, where request distribution occurs in two sequential stages: over a cluster of Web servers and over a cluster of application servers. Much work has focused on strategies for distributing requests across a Web server cluster in order to improve the overall throughput across the cluster. The strategies applied at the application layer are the same as those at the Web server layer because it is assumed that they transfer directly. In this paper, we argue that the problem of distributing requests across an application server cluster is fundamentally different from the Web server request distribution problem due to core differences in request processing in Web and application servers. We devise an approach for distributing requests across a cluster of application servers such that the overall system throughput is enhanced, and load across the application servers is balanced.

Modern application infrastructures are based on clustered, multi-tiered architectures, where request distribution occurs in two sequential stages: over a cluster of Web servers, and over a cluster of application servers. Much work has focused on strategies for distributing requests across a Web server cluster in order to improve overall throughput across the cluster. The strategies applied at the application layer are the same as those at the Web server layer, because it is assumed that they transfer directly. In this paper, we argue that the problem of distributing requests across an application server cluster is fundamentally different from the Web server request distribution problem, due to core differences in request processing in Web and application servers. We devise an approach for distributing requests across a cluster of application servers such that overall system throughput is enhanced, and load across the application servers is balanced. We compare the performance of our approach-with widely used industrial and recently proposed techniques from the literature experimentally in terms of throughput and response time performance, as well as resource utilization. Our experimental results show a significant improvement of up to nearly 80% in both throughput and response time, with a very low additional cost in terms of CPU overheads, 0.1% to 1.5%, on the Web server, and virtually no impact on CPU overheads on the application server

As Internet traffic continues to grow and websites become increasingly complex, performance and scalability are major issues for websites. Websites are increasingly relying on dynamic content generation applications to provide website visitors with dynamic, interactive, and personalized experiences. However, dynamic content generation comes at a cost---each request requires computation as well as communication across multiple components.To address these issues, various dynamic content caching approaches have been proposed. Proxy-based caching approaches store content at various locations outside the site infrastructure and can improve website performance by reducing content generation delays, firewall processing delays, and bandwidth requirements. However, existing proxy-based caching approaches either (a) cache at the page level, which does not guarantee that correct pages are served and provides very limited reusability, or (b) cache at the fragment level, which is associated with several design-level and runtime scalability issues. To address these issues, several back-end caching approaches have been proposed, including query result caching and fragment level caching. While back-end approaches guarantee the correctness of results and offer the advantages of fine-grained caching, they neither address firewall delays nor reduce bandwidth requirements.In this article, we present an approach and an implementation of a dynamic proxy caching technique which combines the benefits of both proxy-based and back-end caching approaches, yet does not suffer from their above-mentioned limitations. Our dynamic proxy caching technique allows granular, proxy-based caching in highly dynamic scenarios, accessible outside the site infrastructure. We present two possible configurations for our dynamic proxy caching technique: (1) a reverse proxy configuration, and (2) a forward proxy configuration. Analysis of the performance of our approach indicates that it is capable of providing significant reductions in bandwidth. We have deployed our proposed dynamic proxy caching technique at a major financial institution. The results of this implementation indicate that our technique is capable of providing up to 3x reductions in bandwidth and response times in real-world dynamic Web applications when compared to existing caching solutions.

With the expansion of Web sites to include business functions, a user interfaces with e-businesses through an interactive and multistep process, which is often time-consuming. For mobile users accessing the Web over digital cellular networks, the failure of the wireless link, a frequent occurrence, can result in the loss of work accomplished prior to the disruption. This work must then be repeated upon subsequent reconnection - often at significant cost in time and computation. This "disconnection-reconnection-repeat work" cycle may cause mobile clients to incur substantial monetary as well as resource (such as battery power) costs. In this paper, we propose a protocol for "recovering" a user to an appropriate recent interaction state after such a failure. The objective is to minimize the amount of work that needs to be redone upon restart after failure. Whereas classical database recovery focuses on recovering the system, i.e., all transactions, our work considers the problem of recovering a particular user interaction with the system. This recovery problem encompasses several interesting subproblems: (1) modeling user interaction in a way that is useful for recovery, (2) characterizing a user's "recovery state", (3) determining the state to which a user should be recovered, and (4) defining a recovery mechanism. We describe the user interaction with one or more Web sites using intuitive and familiar concepts from database transactions. We call this interaction an Internet transaction (iTX), distinguish this notion from extant transaction models, and develop a model for it, as well as for a user's state on a Web site. Based on the twin foundations of our iTX and state models, we finally describe an effective protocol for recovering users to valid states in Internet interactions.

As Internet traffic continues to grow and web sites become increasingly complex, performance and scalability are major issues for web sites. Web sites are increasingly relying on dynamic content generation applications to provide web site visitors with dynamic, interactive, and personalized experiences. However, dynamic content generation comes at a cost --- each request requires computation as well as communication across multiple components.To address these issues, various dynamic content caching approaches have been proposed. Proxy-based caching approaches store content at various locations outside the site infrastructure and can improve Web site performance by reducing content generation delays, firewall processing delays, and bandwidth requirements. However, existing proxy-based caching approaches either (a) cache at the page level, which does not guarantee that correct pages are served and provides very limited reusability, or (b) cache at the fragment level, which requires the use of pre-defined page layouts. To address these issues, several back end caching approaches have been proposed, including query result caching and fragment level caching. While back end approaches guarantee the correctness of results and offer the advantages of fine-grained caching, they neither address firewall delays nor reduce bandwidth requirements.In this paper, we present an approach and an implementation of a dynamic proxy caching technique which combines the benefits of both proxy-based and back end caching approaches, yet does not suffer from their above-mentioned limitations. Our dynamic proxy caching technique allows granular, proxy-based caching where both the content and layout can be dynamic. Our analysis of the performance of our approach indicates that it is capable of providing significant reductions in bandwidth. We have also deployed our proposed dynamic proxy caching technique at a major financial institution. The results of this implementation indicate that our technique is capable of providing order-of-magnitude reductions in bandwidth and response times in real-world dynamic Web applications.

On-line analytical processing (OLAP) refers to the technologies that allow users to efficiently retrieve data from the data warehouse for decision-support purposes. Data warehouses tend to be extremely large, it is quite possible for a data warehouse to be hundreds of gigabytes to terabytes in size (Chauduri and Dayal, 1997). Queries tend to be complex and ad hoc, often requiring computationally expensive operations such as joins and aggregation. Given this, we are interested in developing strategies for improving query processing in data warehouses by exploring the applicability of parallel processing techniques. In particular, we exploit the natural partitionability of a star schema and render it even more efficient by applying DataIndexes-a storage structure that serves both as an index as well as data and lends itself naturally to vertical partitioning of the data. DataIndexes are derived from the various special purpose access mechanisms currently supported in commercial OLAP products. Specifically, we propose a declustering strategy which incorporates both task and data partitioning and present the Parallel Star Join (PSJ) Algorithm, which provides a means to perform a star join in parallel using efficient operations involving only rowsets and projection columns. We compare the performance of the PSJ Algorithm with two parallel query processing strategies. The first is a parallel join strategy utilizing the Bitmap Join Index (BJI), arguably the state-of-the-art OLAP join structure in use today. For the second strategy we choose a well-known parallel join algorithm, namely the pipelined hash algorithm. To assist in the performance comparison, we first develop a cost model of the disk access and transmission costs for all three approaches.

Web sites allow the collection of vast amounts of navigational data -- clickstreams of user traversals through the site. These massive data stores offer the tantalizing possibility of uncovering interesting patterns within the dataset. For e-businesses, always looking for an edge in the hyper-competitive online marketplace, this possibility is of particular interest. Of significant particular interest to e-businesses is the discovery of Critical Edge Sequences (CES), which denote frequently traversed subpaths in the catalog. CESs can be used to improve site performance and site management, increase the effectiveness of advertising on the site, and gather additional knowledge of customer interest patterns on the site.Using traditional graph-based and web mining strategies to find CESs could turn out to be expensive in both space and time. In this paper, we propose a method to compute the most popular paths bewteen node pairs in a catalog, which are then used to discover CESs. Our method is both space-efficient and accurate, providing a vast reduction in the storage requirement with a minimum impact on accuracy. This algorithm, executed off-line in batch mode, is also practical with respect to running time. As a variant of single-source shortest-path, it runs in log linear time.

No abstract available.

Online personalization is of great interest to e-companies. Virtually all personalization technologies are based on the idea of storing as much historical customer session data as possible, and then querying the data store as customers navigate through a web site. The holy grail of on-line personalization is an environment where fine-grained, detailed historical session data can be queried based on current online navigation patterns to formulate real-time responses. Unfortunately, as more consumers become e-shoppers, the user load and the amount of historical data continue to increase, causing scalability-related problems for almost all current personalization technologies. This paper chronicles the development of a real-time interaction management engine through the integration of historical data and on-line visitation patterns of e-commerce site visitors. This paper describes the scientific underpinnings of the system, as well as the architecture and a performance evaluation....

Mobile computing has the potential for managing information globally. Data management issues in mobile computing have received some attention in recent times, and the design of adaptive braodcast protocols has been posed as an important probllem. Such protocols are employed by database servers to decide on the content of bbroadcasts dynamically, in response to client mobility and demand patterns. In this paper we design such protocols and also propose efficient retrieval strategies that may be employed by clients to download information from broadcasts. The goal is to design cooperative strategies between server and client to provide access to information in such a way as to minimize energy expenditure by clients. We evaluate the performance of our protocols both analytically and through simulation.

Mobile computing has the potential for managing information globally. Data management issues in mobile computing have received some attention in recent times, and the design of adaptive broadcast protocols has been posed as an important problem. Such protocols are employed by database servers to decide on the content of broadcasts dynamically, in response to client mobility and demand patterns. In this paper we design such protocols and also propose efficient retrieval strategies that may be employed by clients to download information from broadcasts. The goal is to design cooperative strategies between server and client to provide access to information in such a way as to minimize energy expenditure by clients. We evaluate the performance of our protocols analytically.