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This paper studies the spatial group-by query over complex polygons. Given a set of spatial points and a set of polygons, the spatial group-by query returns the number of points that lie within the boundaries of each polygon. Groups are selected from a set of non-overlapping complex polygons, typically in the order of thousands, while the input is a large-scale dataset that contains hundreds of millions or even billions of spatial points. This problem is challenging because real polygons (like counties, cities, postal codes, voting regions, etc.) are described by very complex boundaries. We propose a highly-parallelized query processing framework to efficiently compute the spatial group-by query on highly skewed spatial data. We also propose an effective query optimizer that adaptively assigns the appropriate processing scheme based on the query polygons. Our experimental evaluation with real data and queries has shown significant superiority over all existing techniques. [ABSTRACT FROM AUTHOR]

Publish-subscribe systems present the state of the art in information dissemination to multiple users. Such systems have evolved from simple topic-based to the current XML-based systems. XML-based pubsub systems provide users with more flexibility by allowing the formulation of complex queries on the content as well as the structure of the streaming messages. Messages that match a given user query are forwarded to the user. This article examines how to exploit the parallelism found in XPath filtering. Using an incoming XML stream, parsing and matching thousands of user profiles are performed simultaneously by matching engines. We show the benefits and trade-offs of mapping the proposed filtering approach onto FPGAs, processing streams of XML at wire speed, and GPUs, providing the flexibility of software. This is in contrast to conventional approaches bound by the sequential aspect of software computing, associated with a large memory footprint. By converting XPath expressions into custom stacks, our solution is the first to provide support for complex XPath structural constructs, such as parent-child and ancestor descendant relations, whilst allowing wildcarding and recursion. The measured speedups resulting from the GPU and FPGA accelerations versus single-core CPUs are up to 6.6X and 2.5 orders of magnitude, respectively. The FPGA approaches are up to 31X faster than software running on 12 CPU cores. [ABSTRACT FROM AUTHOR]

Computing temporal aggregates is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.) Due to the large volume of such data, performance improvements for temporal aggregate queries are critical. Previous approaches have aggregate predicates that involve only the time dimension. In this article we examine techniques to compute temporal aggregates that include key-range predicates as well (range-temporal aggregates). In particular we concentrate on the SUM aggregate, while COUNT is a special case. To handle arbitrary key ranges, previous methods would need to keep a separate index for every possible key range. We propose an approach based on a new index structure called the Multiversion SB-Tree, which incorporates features from both the SB-Tree and the Multiversion B+-tree, to handle arbitrary key-range temporal aggregate queries. We analyze the performance of our approach and present experimental results that show its efficiency. Furthermore, we address a novel and practical variation called functional range-temporal aggregates. Here, the value of any record is a function over time. The meaning of aggregates is altered such that the contribution of a record to the aggregate result is proportional to the size of the intersection between the record's time interval and the query time interval. Both analytical and experimental results show the efficiency of our result. [ABSTRACT FROM AUTHOR]

Managing multiple versions of XML documents represents a critical requirement for many applications. Recently, there has been much work on supporting complex queries on XML data (e.g., regular path expressions, structural projections, etc.). In this article, we examine the problem of implementing efficiently such complex queries on multiversion XML documents. Our approach relies on a numbering scheme, whereby durable node numbers (DNNs) are used to preserve the order among the nodes of the XML tree while remaining invariant with respect to updates. Using the document's DNNs, we show that many complex queries are reduced to combinations of range version retrieval queries. We thus examine three alternative storage organizations/indexing schemes to efficiently evaluate range version retrieval queries in this environment. A thorough performance analysis is then presented to reveal the advantages of each scheme. [ABSTRACT FROM AUTHOR]

Introduces several articles about multimedia systems. Scheduling algorithms for maximizing profits of a broadcast-based electronic delivery service for purchasing digital products; On-demand multicast delivery of streaming media; Approach for indexing animated objects; Construction of communication subgraphs for a distributed virtual environment system.

Today, data is being actively generated by a variety of devices, services, and applications. Such data is important not only for the information that it contains, but also for its relationships to other data and to interested users. Most existing Big Data systems focus on passively answering queries from users, rather than actively collecting data, processing it, and serving it to users. To satisfy both passive and active requests at scale, application developers need either to heavily customize an existing passive Big Data system or to glue one together with systems like Streaming Engines and Pub-sub services . Either choice requires significant effort and incurs additional overhead. In this paper, we present the BAD (Big Active Data) system as an end-to-end, out-of-the-box solution for this challenge. It is designed to preserve the merits of passive Big Data systems and introduces new features for actively serving Big Data to users at scale. We show the design and implementation of the BAD system, demonstrate how BAD facilitates providing both passive and active data services, investigate the BAD system's performance at scale, and illustrate the complexities that would result from instead providing BAD-like services with a "glued" system., (© The Author(s) 2022.)

Virtually all of today's Big Data systems are passive in nature. Here we describe a project to shift Big Data platforms from passive to active . We detail a vision for a scalable system that can continuously and reliably capture Big Data to enable timely and automatic delivery of new information to a large pool of interested users as well as supporting analyses of historical information. We are currently building a Big Active Data (BAD) system by extending an existing scalable open-source BDMS (AsterixDB) in this active direction. This first paper zooms in on the Data Serving piece of the BAD puzzle, including its key concepts and user model.