7867063
D-CAPE: distributed and self-tuned continuous query processing
Efficient continuous query processing is critical for many applications, including monitoring remote sensors network traffic management, and online transaction processing. To overcome resource limitations and achieve real-time responsiveness, continuous query systems research focuses on issues such as punctuation-driven operator-state purging [4], load shedding, operator scheduling [7] and plan optimization and on-line migration [9]. However, when real-time yet accurate results are critical such as in stock market analysis, then there is a limitation to what any of the above optimization techniques can accomplish. Hence, distributed processing must be applied to support required scalability. It has been already proven in traditional database systems [3] that distributed processing results in high scale-up and speed-up capabilities due to aggregated resources. The design of a distributed continuous query system is characterized by an extra complexity, considered in the design of our D-Cape engine. In such a system, data streams may be infinite and initial cost statistics about the data streams are typically unknown. Moreover, cost statistics continue to change over time. Our research on distributed stream processing addresses two critical questions: (1) How to initially distribute query plans given little or possibly no cost information, and (2) How to efficiently adapt the query distribution corresponding to runtime environmental changes. Even though research is now under way in designing distributed continuous query systems [2], [1], results to date, as far as we know, are based on simulations. Our work offers empirical results of distributed continuous query processing using an actual software system [5], [6]. D-Cape, a distributed continuous query processing architecture, employs stream query engines over a cluster of shared-nothing processors. We employ a dedicated distribution manager to manage
14837083
A Transactional Approach to Parallel Data Warehouse Maintenance
Data Warehousing is becoming an increasingly important technology for information integration and data analysis. Given the dynamic nature of modern distributed environments, both source data and schema changes are likely to occur autonomously and even concurrently in different sources. We have thus developed a comprehensive solution approach, called TxnWrap, that successfully maintains the warehouse views under any type of concurrent source updates. In this work, we now overcome TxnWrap's restriction that the maintenance is processed one by one for each source update, since that limits the performance. To overcome this limitation, we exploit the transactional approach of TxnWrap to achieve parallel data warehouse maintenance. For this, we first identify the read/write conflicts among the different warehouse maintenance processes. We then propose a parallel maintenance scheduler (PMS) that generates legal schedules that resolve these conflicts. PMS has been implemented and incorporated into our TxnWrap system. The experimental results confirm that our parallel maintenance scheduler significantly improves the performance of data warehouse maintenance.
3419561
A Dynamically Adaptive Distributed System for Processing Complex Continuous Queries
Recent years have witnessed rapidly growing research attention on continuous query processing over streams [2, 3]. A continuous query system can easily run out of resources in case of large amount of input stream data. Distributed continuous query processing over a shared nothing architecture, i.e., a cluster of machines, has been recognized as a scalable method to solve this problem [2, 8, 9]. Due to the lack of initial cost information and the fluctuating nature of the streaming data, uneven workload among machines may occur and this may impair the benefits of distributed processing. Thus dynamic adaptation techniques are crucial for a distributed continuous query system.
11333656
Efficient decoupling capacitor planning via convex programming methods
Achieving P/G supply signal integrity is crucial to success of nanometer VLSI designs. Existing P/G network optimization techniques are dominated by sensitivity based approaches. In this paper, we propose two novel convex programming based approaches for decoupling capacitor insertion in a P/G network, i.e., a semidefinite program and a linear program, which are global optimizations with theoretically guaranteed supply voltage degradation bounds. We also propose a scalability improvement scheme which enables us to apply the proposed convex programs to industry designs. We present a simple illustrative example and experimental results on an industry design, which show that the proposed semidefinite program guarantees supply voltage degradation bound for all possible supply current sources, while the proposed linear program achieves the most accurate supply voltage degradation control for a given set of supply current sources.
10079260
The sum number and integral sum number of complete bipartite graphs
Let N denote the set of positive integers. The sum graph G + (S) of a 1nite subset S of N is the graph (S; E) with vertex set S and edge set E such that for u; v ∈ S; uv ∈ E if and only if u + v ∈ S. A graph G is called a sum graph if it is isomorphic to the sum graph G + (S) of some 1nite subset S of N . The sum number � (G) of a graph G is de1ned as the smallest nonnegative integer m for which G ∪ mK1 is a sum graph. Let Z be the set of all integers. By extending the set N to Z in the above de1nitions of sum graphs and sum numbers, Harary [3] introduced the corresponding notions of integral sum graphs and integral sum number of a graph. In this paper, we evaluate the value of the sum number and integral sum number of the complete bipartite graph Kr; s. While the former one corrects the result given in [4], the latter settles completely a problem proposed in [3]. c � 2001 Elsevier Science B.V. All rights reserved.
8364982
Supporting Complex Multi-Dimensional Queries in P2P Systems
More and more applications require peer-to-peer (P2P) systems to support complex queries over multi-dimensional data. For example, a P2P auction network for real estate frequently needs to answer queries such as "select five available buildings closest to the airport". Such queries are not efficiently supported in current P2P systems. Towards an efficient and scalable P2P system capable of processing complex multi-dimensional queries, the authors first proposed a comprehensive framework for sharing, indexing, and querying multi-dimensional data, where (i) peers with more computational power coordinate indexing and query processing, and (ii) other peers participate in part of the computation in order to achieve scalability and load-balance. Based on this framework, Network-R-tree (NR-tree), a P2P adaptation of the dominant spatial index - R*-tree was proposed. NR-tree, indexing spatial data at clustered peers, is capable of processing complex queries such as range queries and k-nearest neighbor queries. The authors proposed query processing algorithms for range and k-nearest neighbor queries and experimentally prove the effectiveness of proposed techniques with real data
8584874
Throughput Optimization and Fair Bandwidth Allocation in Multi-Hop Wireless LANs
There is an inherent well-known conflict between fairness and throughput that arises in many networking scenarios. A number of researchers have studied this problem in the context of (single-hop) wireless local area networks (WLANs), where clients directly exchange traffic with access points (APs). More recently, researchers have proposed multi-hop extensions to WLANs where client traffic is forwarded via a series of client-client links. In this paper, we show that the objective of improving throughput without sacrificing fairness can be much better met in multi-hop WLANs. We decouple this objective into two separate but related problems. First, we need an algorithm to organize clients into a multi-hop structure such that fair bandwidth allocation within this structure leads to improved throughput. Second, we need algorithms for performing fair bandwidth allocation within the determined multi-hop structure. In this paper, we first design optimal fair bandwidth allocation algorithms for both max-min throughput fairness and max-min time fairness in multi-hop WLANs. Subsequently, we design an efficient algorithm to find desirable multi-hop structures. With slight modifications, our results in this paper can be generalized to other multi-hop wireless networks as well. Our proposed solutions seamlessly integrate with legacy devices and hence are incrementally deployable. Simulation results demonstrate that our solutions can effectively improve throughput (by up to 114% or more) as well as network coverage while preserving fairness.
1259464
Using Temporal Semantics for Live Media Stream Queries
Querying live media streams is a challenging problem that becomes an essential requirement in a growing number of applications. We address the problem of evaluating continuous queries on media streams produced by media sources such as webcams and microphones. The temporal attributes and the order of stream tuples play essential roles in live stream generation and query execution. Furthermore, the temporal constraints and query semantics of related streams provide additional query optimization opportunities. We investigate the modeling issues and introduce the query processing techniques of a live media stream management system (MedSMan), including media capturing, automatic feature generating, declaration and query languages, temporal stream operators and querying algorithms. A prototype is implemented and we present experimental results to show the performance of our prototype using various real-time media experiments.