Worldwide epidemics, such as corona virus disease 2019 (COVID-19), cause unprecedented challenges for society and its healthcare systems. Governments attempt to mitigate those challenges by either reducing healthcare demand (“flattening the curve” by imposing restrictions, e.g., on travel or social gatherings) or by increasing healthcare capacity, for example, by canceling elective procedures or setting up field hospitals. To implement these mitigation procedures efficiently, accurate and timely forecasts of the epidemic’s progression are necessary. In this paper, we develop an innovative forecasting methodology based on the ideas of long short-term memory (LSTM) recurrent neural networks. LSTM models are shown to outperform traditional forecasting models, especially when the relationship between input and output is complex and not available in closed form. However, whereas LSTM models perform well for data that changes dynamically over time, one shortcoming is that they are not directly applicable when the data also includes static, nontemporal components. In this work, we propose an [Formula: see text] model that overcomes this limitation. Our model leverages a private partnership with a mobile data company in order to capture population mobility (using mobility indices derived from mobile device data), which allows us to anticipate an epidemic’s spread early and accurately. In addition, we also leverage a public partnership with a consortium of hospitals. Using hospital admissions (rather than, say, positive caseload) results in an unbiased measure of the severity of an epidemic because patients seek and are admitted to hospital care only when symptoms worsen beyond a critical point. We illustrate the effectiveness of our method on forecasting COVID-19 for a major U.S. metropolitan area where it has aided decision makers of the emergency policy group. Our model improves the predictive accuracy of hospital admission by a factor of 2.5× as compared with competing models in the same analytical space. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This research was funded by a monetary gift from Hillsborough County to establish the Pandemic Response Research Fund at University of South Florida. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1269 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0027 ) at ( http://dx.doi.org/10.5281/zenodo.7112004 ).

Feature selection is a fundamental problem in online advertising, as features usually need to be purchased from third parties, and they are costly. Although many feature selection techniques can be used in online advertising and the general information systems (IS) domain, their performance is often context specific. Therefore, the literature of IS is suffering from a lack of adequate and generic methods. In this study, we address this issue by proposing a novel approach that employs ideas from the field of cooperative game theory. We derive a (continuous) second-order cone program that any convex programming solver can solve for determining the best subset of features. We show the efficacy of our proposed method on a real-life online advertising case study. We demonstrate that our proposed approach performs better in accuracy, precision, recall, and F-1 score than the best of the other approaches with much fewer features. Also, to illustrate that our method’s benefits are not limited to the context of online advertising, we perform an extensive set of simulations and consider a well-established real-life data set drawn from the UCI Machine Learning Repository at the University of California, Irvine. Summary of Contribution: Selecting the best subset of features is an important problem in the context of online advertising and, more broadly, in the field of information systems because firms usually need to buy costly data to model and forecast economic outcomes. In this study, we propose a novel methodology for addressing this problem. The proposed method employs the concept of the Nash bargaining solution in cooperative game theory to create a good balance between maximizing the fit while minimizing the noise when selecting the best subset of features. We apply the method to a real-life online advertising case study, providing superior performance in predicting and interpreting the features. Moreover, we show that the proposed method applies to a broader range of feature selection problems. We conduct a comprehensive computational study on simulated regression data sets and other real-life classification data sets widely available in the machine learning domain. The result of these efforts indicates that our method is robust in terms of prediction accuracy by outperforming several state-of-the-art techniques.

Real-time bidding (RTB) for digital advertising is becoming the norm for improving advertisers’ campaigns. Unlike traditional advertising practices, in the process of RTB, the advertisement slots of a mobile application or a website are mapped to a particular advertiser through a real-time auction. The auction is triggered and is held for a few milliseconds after an application is launched. As one of the key components of the RTB ecosystem, the demand-side platform gives the advertisers a full pledge window to bid for available impressions. Because of the fast-growing market of mobile applications and websites, the selection of the most pertinent target audience for a particular advertiser is not a simple human-mediated process. The real-time programmatic approach has become popular instead. To address the complexity and dynamic nature of the RTB process, we propose an auto pricing strategy (APS) approach to determine the applications to bid for and their respective bid prices from the advertising agencies’ perspective. We apply the APS to actual RTB data and demonstrate how it outperforms the existing RTB approaches with a higher conversion rate for a lower target spend. A video abstract is available at https://doi.org/10.1287/ijoc.2018.0812 .

The ageing of the global population is creating a crisis in chronic disease management. In the USA, 29 million people (or 9.3% of the population) suffer from the chronic disease of diabetes; according to the WHO, globally around 200 million people are diabetic. Left unchecked, diabetes can lead to acute and long-term complications and ultimately death. Diabetes prevalence tends to be the highest among those aged 65 and older (nearly 20.6%), a population which often lacks the cognitive resources to deal with the daily self-management regimens. In this paper, we discuss the design and implementation of an Internet-of-Things (IoT) and wireless sensor system which patients use in their own homes to capture daily activity, an important component in diabetes management. Following Fogg’s 2009 persuasion theory, we mine the activity data and provide motivational messages to the subjects with the intention of changing their activity and dietary behaviour. We introduce a novel idea called “persuasive sensing” and report results from two home implementations that show exciting promise. With the captured home monitoring data, we also develop analytic models that can predict blood glucose levels for the next day with an accuracy of 94%. We conclude with lessons learned from these two home case studies and explore design principles for creating novel IoT systems.

The explosive growth of mobile apps makes it difficult for users to find their needed apps in a crowded market. An effective mechanism that provides high quality app recommendations becomes necessary. However, existing recommendation techniques tend to recommend similar items but fail to consider users’ functional requirements, making them not effective in the app domain. In this article, we propose a recommendation architecture that can generate app recommendations at the functionality level. We address the redundant recommendation problem in the app domain by highlighting users’ functional requirements, an element that has received scant attention from existing recommendation research. Another main feature of our work is extracting app functionalities from textural user reviews for recommendation. We also propose an effective approach for functionality extraction. Experiments conducted on a real‐world dataset show that our proposed AppRank method outperforms other commonly used recommendation methods. In particular, it doubles the recall value of the second best method under an extremely sparse setting, increases the overall ranking accuracy of the second best method by 14.27%, and retains a high diversity of 0.99.

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.

Sentiment classification is one of the most extensively studied problems in sentiment analysis, and supervised learning methods, which require labeled data for training, have been proven quite effective. However, supervised methods assume that the training domain and the testing domain share the same distribution; otherwise, accuracy drops dramatically. Although this does not pose problems when training data are readily available, in some circumstances, labeled data is quite expensive to acquire. For instance, if we want to detect sentiment from Tweets or Facebook comments, the only way to acquire is to manually label it, and this is prohibitively burdensome and time-consuming. In this paper, we propose a hybrid approach that integrates the sentiment information from source-domain labeled data and a set of preselected sentiment words to solve this problem. The experimental results suggest that our method statistically outperforms the state of the art and even, in some cases, surpasses the in-domain gold standard.

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.

Diabetes is a common but serious chronic disease. Nearly 8% of Americans who are aged 65 and older (about 10.9 million) suffer from this deadly disease. Self-management of this disease is possible, yet the older population lack knowledge, have denial and often lack motivation to do so. Recently we have demonstrated sensor-based network architecture within the home to monitor daily activities and biological vital parameters [25]. The data is mined to find patterns and abnormal values. Through daily text messages that are sent to the subjects, we have achieved to influence behavior change using persuasive principles. In this paper, we analyze the daily data and demonstrate that a model to profile the subject's daily behavior is possible using Artificial Neural Networks (ANN). Such a profiling has the advantage of knowing the situations, when the subject's daily activity deviates from its “normal profile”, which may be a possible indication of an onset of some health condition or disease. Lastly we develop an ANN based model to predict blood sugar level based on previous day's activity and diet intake. Such a model can be used to help a subject with high blood sugar to adjust daily activity to reach a target blood sugar level and also gives a care-giver advance notice to intervene in adverse situations.

With the growing adoption of virtualized datacenters and cloud hosting services, the allocation and sizing of resources such as CPU, memory, and I/O bandwidth for virtual machines (VMs) is becoming increasingly important. Accurate performance modeling of an application would help users in better VM sizing, thus reducing costs. It can also benefit cloud service providers who can offer a new charging model based on the VMs' performance instead of their configured sizes. In this paper, we present techniques to model the performance of a VM-hosted application as a function of the resources allocated to the VM and the resource contention it experiences. To address this multi-dimensional modeling problem, we propose and refine the use of two machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). We evaluate these modeling techniques using five virtualized applications from the RUBiS and Filebench suite of benchmarks and demonstrate that their median and 90th percentile prediction errors are within 4.36% and 29.17% respectively. These results are substantially better than regression based approaches as well as direct applications of machine learning techniques without our refinements. We also present a simple and effective approach to VM sizing and empirically demonstrate that it can deliver optimal results for 65% of the sizing problems that we studied and produces close-to-optimal sizes for the remaining 35%.

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.

Performance models provide the ability to predict application performance for a given set of hardware resources and are used for capacity planning and resource management. Traditional performance models assume the availability of dedicated hardware for the application. With growing application deployment on virtualized hardware, hardware resources are increasingly shared across multiple virtual machines. In this paper, we build performance models for applications in virtualized environments. We identify a key set of virtualization architecture independent parameters that influence application performance for a diverse and representative set of applications. We explore several conventional modeling techniques and evaluate their effectiveness in modeling application performance in a virtualized environment. We propose an iterative model training technique based on artificial neural networks which is found to be accurate across a range of applications. The proposed approach is implemented as a prototype in Xen-based virtual machine environments and evaluated for accuracy, sensitivity to the training process, and overhead. Median modeling error in the range 1.16-6.65% across a diverse application set and low modeling overhead suggest the suitability of our approach in production virtualized environments.

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.

Database storage management at data centers is a manual, time-consuming, and error-prone task. Such management involves regular movement of database objects across storage nodes in an attempt to balance the I/O bandwidth utilization across disk drives. Achieving such balance is critical for avoiding I/O bottlenecks and thereby maximizing the utilization of the storage system. However, manual management of the aforesaid task, apart from increasing administrative costs, encumbers the greater risks of untimely and erroneous operations. We address the preceding concerns with STORM, an automated approach that combines low-overhead information gathering of database access and storage usage patterns with efficient analysis to generate accurate and timely hints for the administrator regarding data movement operations. STORM's primary objective is minimizing the volume of data movement required (to minimize potential down-time or reduction in performance) during the reconfiguration operation, with the secondary constraints of space and balanced I/O-bandwidth-utilization across the storage devices. We analyze and evaluate STORM theoretically, using a simulation framework, as well as experimentally. We show that the dynamic data layout reconfiguration problem is NP-hard and we present a heuristic that provides an approximate solution in O ( Nlog ( N / M ) + ( N / M ) 2 ) time, where M is the number of storage devices and N is the total number of database objects residing in the storage devices. A simulation study shows that the heuristic converges to an acceptable solution that is successful in balancing storage utilization with an accuracy that lies within 7% of the ideal solution. Finally, an experimental study demonstrates that the STORM approach can improve the overall performance of the TPC-C benchmark by as much as 22%, by reconfiguring an initial random, but evenly distributed, placement of database objects.

Database storage management in clustered storage environments is a manual, time-consuming, and error-prone task. Such management involves regular movement of database objects across nodes in the storage cluster so that storage utilization is maximized. We present STORM, an automated approach that guides this task by combining low-overhead information gathering about database access and storage usage patterns, efficient analysis of gathered information, and effective decision-making for reconfiguring data layout. The reconfiguration process is guided by the primary optimization objective of minimizing the total data movement required for the reconfiguration, with the secondary constraints of space and balanced I/O bandwidth utilizations across the storage nodes in the cluster. We model the reconfiguration decision-making as a multi-constraint optimization problem which is NP-hard. We then present a heuristic that provides an approximate solution in O(Nlog(N/M) + (N/M)2) time, where M is the number of storage nodes and N is the total number of database objects. A simulation study shows that the heuristic converges to an acceptable solution that is successful in balancing storage utilization with an accuracy that lies within 7% of the ideal solution.

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.

Object-oriented (OO) technologies have become widely adopted in enterprise applications due to the additional functionality and flexibility they provide to these applications. At the same time, however, OO technologies also require significant amounts of computational power to support, greatly impacting the performance and scalability of such applications. A very popular solution to mitigate this problem is object caching. In this paper, we show how the application of object caching maps into an optimization problem. In particular, we focus on the design-time decision of determining which objects should be candidates for caching. Choosing the cacheable objects is an important decision since it can have a significant impact on application performance. We formulate this problem as a linear integer program and present a heuristic solution approach. We also demonstrate, through a set of experiments, that our heuristic provides solutions that are reasonably close to optimal. Our contribution is a model and an efficient solution approach for this model that can help application developers to make more informed cacheability decisions and thereby improve application performance and scalability.

Modern applications are built using object-oriented (OO) principles. Unfortunately, OO technologies, though they add tremendous functionality and flexibility to applications, require significant computational power, which greatly affects the performance and scalability of the applications. Caching objects in memory is a popular way to solve this problem, but the available memory in systems is limited and not sufficient to store each instance of every enterprise application object. Deciding which instances of which objects should be cached is a two-stage process. The first stage is selecting the objects, and the second stage is selecting the object instances. The second stage, typically referred to as a replacement policy, occurs at run-time and has been widely studied. The focus of this paper is the first stage: selecting objects whose instances are candidates for caching at run-time. In current systems, this critical step is performed in an ad hoc manner. We build a nonlinear mathematical model for this problem, develop a feasible solution procedure based on genetic algorithms, and demonstrate its effectiveness by comparing the solution quality to lower bounds obtained using a Lagrangian-relaxation-based method.

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.

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.

Information systems are increasingly becoming more interactive and dynamic in nature. Consider, for instance. the Internet, where web sites not only allow site visitors to purchase items and conduct banking online, but also serve pages that are personalized for individual site visitors. Such functionality is enabled by dynamic content generation technologies. In this paradigm, a user request is sent to a content server, which runs a program. This program accesses a set of data sources, creates a user deliverable object, and then returns this object to the user. In the context of the Internet, for example, a user may request the sports page in a news site. In this case, the server at the news site executes a program, which generates the user deliverable object-an HTML document corresponding to the requested sports page. There has been a significant amount of work on caching in the context of the Internet. However, most of this work is focused on caching rich content (e.g., images, multimedia objects) or caching at coarse granularities, such as HTML pages. A severe drawback of such coarse granularity caching is that the potential for reuse is often very limited. For instance, even though there may be significant reusability in the lower level objects on a page, the top level object (e.g., HTML document) is often unique (e.g., as in the case of a personalized page). In this paper, we address this problem by proposing a cost/benefit analytical model framework and a set of optimization techniques, which together will help identify the "optimal" set of cacheable objects. Our work will extend the application of caching in the context of the Internet to a much wider variety of objects. Our proposed approach entails the following steps: We will develop an object data model to represent dynamically generated web content; Based on this object model, we will propose a cost/benefit analytical framework, which can be used to measure the costs and benefits associated with caching objects; Based on this object model and cost/benefit framework, we will propose a number of optimization approaches to extract the "optimal" set of cacheable objects; Finally, we will present the results of an extensive performance evaluation of the proposed optimization techniques with regard to both accuracy and efficiency.

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....