In the current era of big data, the amount of data a company can acquire is growing exponentially. However, the data are only meaningful if they are used wisely. This paper introduces Anovos, an open-source library built on top of Apache Spark. It is designed to perform efficient, end-to-end feature engineering at scale (with TBs of Data), and helps implement a systematic and procedural data pipeline with enterprise data at one end and model-ready features at the other. Besides improving the current exploratory data analysis process, we have also introduced a few key innovations in Anovos: the concept of data stability index, a single-metric indication of the stability of an independent variable in a longitudinal way, as well as Feature Explorer and Feature Mapper, powered by semantic similarity-based AI models, in order to solve the cold-start problem of building high-quality predictive features for the model training process.

The increasing availability of GPS-embedded devices has introduced a new dimension in digital market especially location-based services. In practice, the location data is used to understand and predict consumer mobility behavior and trend for various purposes. In this paper, we propose two methodologies to first identify suspicious location from consumer location data and to infer location at both individual device and device to device level based on systematic solution. Using stay-point clustering and suspicious patterns we identified from extensive analysis, 20-30% of records with location were observed to be suspicious. After removing inaccurate location data, we have employed scalable heuristic approach to backfill records with location even for devices that originally had no available location. Our model showed the accuracy within 50 meters at 95thpercentile across different countries, including Japan, Indonesia, India, and the United States with 10-15% increase in the number of records with location and 5-10% increase in new number of devices with location.

Identifying valuable customers as well as retaining them has become key component for any business to succeed in this competitive market. Businesses have also realized that relying solely on its own transactional data, might not be sufficient any longer, to meet the required objectives. There is a need to partner and leverage the power of big data available from the external data sources to add more value. In this paper, we are detailing the methodology of mashing up Mobilewalla's high scale mobile consumer data with one of the world's largest online food delivery company in order to revamp their retention and acquisition strategy. In this deployment, Mobilewalla has helped the client, a) to identify the new potential high impact customers from Mobilewalla ecosystem, and b) to predict the unfavorable transitions such as high impact customers getting churned or falling into low impact category. We observed that correctly identified high impact customers by Mobilewalla' customer acquisition model had 21.41% higher average revenue per user (ARPU) than the expected ARPU from high impact customers. Further, the customer retention model can help the client to spend 80% of their retention budget dollars optimally.

Democratization of information access brought about by digital distribution has resulted in two contradictory phenomena: the ability to personalize consumer experience, and greater anonymity of users. These intensify when information is consumed on mobile devices, particularly because techniques to profile users on desktop web do not work on mobile smart-devices. Yet, the already large and still fast-growing field of mobile advertising require activation of audience segments against mobile advertising campaigns. Of particular importance are age and gender segments of mobile users, as these user characteristics are required for targeting a large number of ad campaigns. To date, there are no practical methodologies available in the literature that allow for accurate identification of age and gender of mobile users, at scale.In this paper, we propose a scalable machine learning approach to infer the age and gender of mobile users. We have successfully tested and implemented the gender prediction model for 8 countries, additionally the groundwork has been laid out for implementation of age inference. The output is integrated with our commercial products, furthermore, it is used as a part of custom client deliveries. We inferred gender for more than 500 million devices and there is a notable increase in number of devices with gender label (post prediction) within our current dataset. We have also inferred age for 17 million devices in Australia.

In the era of digital marketing, it is imperative for brands to reach target segment precisely to maximize their marketing ROI. In most cases, the target segments are created based on broad demographic and psychographic traits without any knowledge of the consumer brand preferences. In this paper, we propose a methodology to predict the individual level consumer brand preference based on the historical brand visitation patterns. We believe that this is the first attempt and a successful deployment to predict individual consumer level brand preference at large scale. In general, it is very hard to accumulate longitudinal location data of consumers. As Mobile Ad ecosystem is one of the major source in generating geospatial and temporal data which provides rich information about the mobility patterns of the mobile devices (addressed as consumers hereafter). We harnessed the power of spatio-temporal data received in our Demand Side Platform which is accumulated over a period of more than 2 years to predict brand preferences. Further, we carefully curated the brands' POI data across different countries to derive the historical brand visitation patterns of consumers. Then, this visitation pattern is used to predict propensity score for a given brand for a given consumer. We have employed a recommender system approach using distributed Alternating Least Squares - Weighted Regularization (ALS-WR) based matrix factorization to predict the brand propensities at scale. Rigorous experiments are conducted to validate the model's performance against the benchmark. Our model showed twice as much lift compared to the benchmark with 40% average recall across the brands for the Indonesian market. The full pipeline is developed and deployed in production for the countries of our business's interest such as Indonesia, Thailand, Philippines, Singapore and Malaysia in our AWS EMR1cloud environment.

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.

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).

Cross-domain text classification aims to automatically train a precise text classifier for a target domain by using labelled text data from a related source domain. To this end, one of the most promising ideas is to induce a new feature representation so that the distributional difference between domains can be reduced and a more accurate classifier can be learned in this new feature space. However, most existing methods do not explore the duality of the marginal distribution of examples and the conditional distribution of class labels given labeled training examples in the source domain. Besides, few previous works attempt to explicitly distinguish the domain-independent and domain-specific latent features and align the domain-specific features to further improve the cross-domain learning. In this paper, we propose a model called Partially Supervised Cross-Collection LDA topic model (PSCCLDA) for cross-domain learning with the purpose of addressing these two issues in a unified way. Experimental results on nine datasets show that our model outperforms two standard classifiers and four state-of-the-art methods, which demonstrates the effectiveness of our proposed model.

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.

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.

Real-time active database systems (RTADBSs) have attracted a considerable amount of research attention in the past and a number of important applications have been identified for such systems, such as telecommunications network management, automated air traffic control, automated financial trading, process control and military command and control systems. In spite of the recognized importance of this area, very little research has been devoted to exploring the dynamics of transaction processing in RTADBSs. Concurrency control (CC) constitutes an integral part of any transaction processing strategy and, thus, deserves special attention. We study CC strategies in RTADBSs and postulate a number of CC algorithms. These algorithms exploit the special needs and features of RTADBSs and are shown to deliver substantially superior performance to conventional real-time CC algorithms.

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.

This paper addresses the problem of providing secure access control in broadcast schemes in a wireless network. We consider an environment where clients subscribe to information objects sent via a broadcast onto a wireless network. In this context, a client should only be able to access its objects of interest for its subscription period, and the security system used must not be easily broken. Existing data broadcasting approaches fail to perform well with increased client loads. In this paper, we propose to use the Drop Groups (DG) protocol, that uses a novel grouping criterion. We present the broadcast organization under DG, and demonstrate that our approach is scalable. We use simulation to compare the DG protocol with existing techniques.

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....

The objective of this paper is to perform an in-depth study on the problem of handling sensor data in rapidly changing environments. Due to the dynamic nature of this data, as well as specific reporting patterns, the transactions that manipulate this data have particular requirements. Specifically, these requirements are recency and temporal consistency. We study strategies to satisfy these requirements, while maintaining predetermined levels of Quality of Service (QOS) in the system. We identify a number of algorithms and perform a detailed performance study. This study enables us to make certain general recommendations regarding the properties of algorithms designed to handle rapidly changing sensor data. Keywords: Sensor Data, Recency, Temporal Consistency, Soft Real-Time, QOS, Performance Evaluation 1 Introduction Database systems that attempt to model and control external environments have attracted the attention of researchers in recent times. The application domains for such...

After a "boom" period from the late 80s to the early 90s, there appears to have been a reduction in the amount of work published on Concurrency Control (CC) in real-time database systems (RTDBS) in general and firm RTDBS in particular. This may be because existing paradigms (e.g., Optimistic CC) have been pushed to their limits and it is difficult to extract additional meaningful performance. One of the last unresolved bastions of real-time CC is the successful incorporation of priority cognizance. Researchers have speculated that priority cognizant optimistic concurrency control (OCC) algorithms, if designed well, could outperform priority insensitive ones in real-time database systems. So far, however, there is a distinct lack of conclusive proof available on this topic and the priority cognizant OCC algorithms that have appeared so far in the literature cannot claim unilateral superiority over their priority insensitive relatives. We thus surmise that successful incorporation of priority cognizance may lead to an increase in the performance of OCC protocols in firm RTDBSs. Based on this premise, we analyze the issue of priority cognizance and identify a critical condition that must hold for priority cognizant conflict resolution to work. The condition is that, on the average, the conflict sets of validating transactions should have a "large" number of transactions: We call this a bird in hand more than two in the bush phenomenon. Subsequently, we design a smart priority cognizant OCC variant, which we call OCC-APR, and analyze its performance, as well as that of several other concurrency control algorithms across a wide range of resource contention and system loading parameters. Surprisingly, it turns out that it is very difficult for priority cognizance to work as the above mentioned condition does not, usually,hold. We explain why this occurs and conclude that priority cognizance does not appear to be a promising technique to increase real-time CC performance. The contribution of this paper, thus, is to have laid to rest the "priority cognizance" issue with regard to real-time CCMs.

Push-based information delivery technology is commonly expressed as a broadcast solution where the information is pushed to a channel where all receivers can tap into. In this context, prior work has explored how to broadcast and what to broadcast. However, one problem not addressed so far is providing secure access control in broadcast schemes. Furthermore, existing broadcast work does not consider a subscription-based access scheme which is conceivable for most commodities. In this paper, we present existing secure access control schemes and adapt them to the broadcasting scenario, and outline their limitations in this environment. We propose a new protocol, single encoding multiple decoding (SEMD), to handle secure access and subscription services for broadcast. The superior performance of SEMD is demonstrated via a a simulation study of existing protocols and our protocol in a mobile computing environment.

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.

Real-time active database systems (RTADB) have attracted the attention of researchers in recent times. Such systems are envisioned as control systems for environments as diverse as process control, network management and automated financial trading. Sensors distributed throughout the system report the state of the system to the database. Unacceptable state reports typically results in corrective actions being triggered with deadlines. Thus RTADB's incorporate both real-time as well as active characteristics. We study buffer management in RTADB. Buffer management is recognized as not being a well studied area in real-time systems. As a result of our work, we postulate PAPER, a new buffer management scheme that relies on two strategies: prefetching and priority based buffer replacement. We report the result of studies of the performance of PAPER, as compared to that of existing buffer management algorithms. The insights derived from this paper impact both real-time database systems as well as real-time, active database systems.

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.

In mobile computing mobile hosts or mobile units move randomly from one place to another within a well-defined geographical area. This requires some mechanism for identifying the exact location of a mobile host. Such mechanisms are usually referred to as location management and is one of the essential components for supporting mobility. In the past a number of efficient location management schemes have been suggested but they lack adaptability. We believe that an adaptable location management scheme is more desirable because of frequent change in the location of mobile hosts. In this paper, we report a location management algorithm, which adapts to the constant changing scenario. Our algorithm makes use of some of the concepts of location management schemes proposed in the past.

There has been a growing demand for the development of tools to manage enterprise communication networks. A management information database is the heart of a network management system-it provides the interface between all functions of the network management system and, therefore, has to provide sophisticated functionality allied with high performance. The authors introduce the design of MANDATE (MAnaging Networks using DAtabase TEchnology), a proposed database system for effectively supporting the management of large enterprise networks. The MANDATE design makes a conscious attempt to take advantage of the special characteristics of network data and transactions, and of advances in database technology, to efficiently derive some of the required management functionality.>