XML documents are frequently used in applications such as business transactions and medical records involving sensitive information. Typically, parts of documents should be visible to users depending on their roles. For instance, an insurance agent may see the billing information part of a medical document but not the details of the patient's medical history. Access control on the basis of data location or value in an XML document is therefore essential. In practice, the number of access control rules is on the order of millions, which is a product of the number of document types (in 1000's) and the number of user roles (in 100's). Therefore, the solution requires high scalability and performance. Current approaches to access control over XML documents have suffered from scalability problems because they tend to work on individual documents. In this paper, we propose a novel approach to XML access control through rule functions that are managed separately from the documents. A rule function is an executable code fragment that encapsulates the access rules (paths and predicates), and is shared by all documents of the same document type. At runtime, the rule functions corresponding to the access request are executed to determine the accessibility of document fragments. Using synthetic and real data, we show the scalability of the scheme by comparing the accessibility evaluation cost of two rule function models. We show that the rule functions generated on user basis is more efficient for XML databases.

An XML publish/subscribe system needs to match many XPath queries (subscriptions) over published XML documents. The performance and scalability of the matching algorithm is essential for the system when the number of XPath subscriptions is large. Earlier solutions to this problem usually built large finite state automata for all the XPath subscriptions in memory. The scalability of this approach is limited by the amount of available physical memory. In this paper, we propose an implementation that uses a relational database as the matching engine. The heavy lifting part of evaluating a large number of subscriptions is done inside a relational database using indices and joins. We described several different implementation strategies and presented a performance evaluation. The system shows very good performance and scalability in our experiments, handling millions of subscriptions with moderate amount of physical memory.

This paper provides an objective evaluation of the performance impacts of binary XML encodings, using a fast stream-based XQuery processor as our representative application. Instead of proposing one binary format and comparing it against standard XML parsers, we investigate the individual effects of several binary encoding techniques that are shared by many proposals. Our goal is to provide a deeper understanding of the performance impacts of binary XML encodings in order to clarify the ongoing and often contentious debate over their merits, particularly in the domain of high performance XML stream processing.

Much attention has been accorded to Location-Based Services and location tracking, a necessary component in active, trigger-based LBS applications. Tracking the location of a large population of moving objects requires very high update and query performance of the underlying spatial index. In this paper we investigate the performance and scalability of three main-memory based spatial indexing methods under dynamic update and query loads: an R-tree, a ZB-tree, and an array/hashtable method. By leveraging the LOCUS performance evaluation testbed and the City Simulator dynamic spatial data generator, we are able to demonstrate the scalability of these methods and determine the maximum population size supported by each method, a useful parameter for capacity planning by wireless carriers.

Tracking the location of people, computers, vehicles, and other mobile objects and performing computation on such location data are quickly gaining interest. The idea of location-based services has captured the imagination of application developers, wireless service providers, and content providers alike. However, the location of people, the most interesting mobile object, is tricky to determine because we don't track persons but rather the devices they carry or the transportation they use. That a person may be associated with numerous tracking devices simultaneously, but perhaps intermittently, makes location tracking challenging. We describe a methodology for aggregating location data from multiple sources associated with a single mobile object. We describe our existing location service infrastructure and illustrate scenarios where location aggregation is desirable and indeed required. Our experimentation in location-based services uses location data from wireless PDAs, vehicle tracking devices, and mobile and stationary computers.

Tempus Fugit (Time Flies) is the first of a new generation of Personal Information Management (PIM) systems. A PIM system incorporates an electronic calendar, list and address book. The premise behind Tempus Fugit is that information stored in electronic calendars, to-do lists and address books can be given richer semantic interpretation and automatically processed to make its users more effective. Tempus Fugit also tracks the physical and virtual locations of users and uses this information to predict meeting attendance and help them as they travel during their day.

Article Effective Web data extraction with standard XML technologies Share on Author: Jussi Myllymaki IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 689–696https://doi.org/10.1145/371920.372183Online:01 April 2001Publication History 34citation1,787DownloadsMetricsTotal Citations34Total Downloads1,787Last 12 Months55Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Despite the steady decrease in secondary storage prices, the data storage requirements of many organizations cannot be met economically using secondary storage alone. Tertiary storage offers a lower-cost alternative but is viewed as a second-class citizen in many systems. For instance, the typical solution in bringing tertiary-resident data under the control of a DBMS is to use operating system facilities to copy the data to secondary storage, and then to perform query optimization and execution as if the data had been in secondary storage all along. This approach fails to recognize the opportunities for saving execution time and storage space if the data were accessed directly on tertiary devices and in parallel with other I/Os. We explore how to join two DBMS relations stored on magnetic tapes. Both relations are assumed to be larger than available disk space. We show how Grace Hash Join can be modified to handle a range of tape relation sizes. The modified algorithms access data directly on tapes and exploit parallelism between disk and tape I/Os. We also provide performance results of an experimental implementation of the algorithms.

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentation of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework is being implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups.

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentations of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework, implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups, makes several contributions. In particular, it combines support for extended relational queries with powerful data visualization features. Datasets much larger than available main memory can be handled—DEVise is currently being used to visualize datasets well in excess of 100MB—and data can be interactively examined at several levels of detail: all the way from meta-data summarizing the entire dataset, to large subsets of the actual data, to individual data records. Combining querying (in general, data processing) with visualizations gives us a very versatile tool, and presents several novel challenges.

We present the design of a log-structured tertiary storage (LTS) system. The advantage of this approach is that it allows the system to hide the details of jukebox robotics and media characteristics behind a uniform, random-access, block-oriented interface. It also allows the system to avoid media mount operations for writes, giving a write performance similar to that of secondary storage.

Today large amounts of data are stored on tertiary storage media such as magnetic tapes and optical disks. DBMSs typically operate only on magnetic disks since they know how to maneuver disks and how to optimize accesses on them. Tertiary devices present a problem for DBMSs since these devices have dismountable media and have very different operational characteristics compared to magnetic disks. For instance, most tape drives offer very high capacity at low cost but are accessed sequentially, involve lengthy latencies, and deliver lower bandwidth. Typically, the scope of a DBMS's query optimizer does not include tertiary devices, and the DBMS might not even know how to control and operate upon tertiary-resident data. In a three-level hierarchy of storage devices (main memory, disk, tape), the typical solution is to elevate tape-resident data to disk devices, thus bringing such data into the DBMS' control, and then to perform the required operations on disk. This requires additional space on disk and may not give the lowest response time possible. With this challenge in mind, we studied the trade-offs between memory and disk requirements and the execution time of a join with the help of two well-known join methods. The conventional, disk-based Nested Block Join and Hybrid Hash Join were modified to operate directly on tapes. An experimental implementation of the modified algorithms gave us more insight into how the algorithms perform in practice. Our performance analysis shows that a DBMS desiring to operate on tertiary storage will benefit from special algorithms that operate directly on tape-resident data and take into account and exploit the mismatch in disk and tape characteristics.