06431 Abstracts Collection -- Scalable Data Management in Evolving Networks

From 22.10.06 to 27.10.06, the Dagstuhl Seminar 06431 ``Scalable Data Management in Evolving Networks\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Managing Data Replication in Mobile Ad-Hoc Network Databases

A Mobile Ad-hoc Network (MANET) is a collection of wireless autonomous nodes without any fixed backbone infrastructure. All the nodes in MANET are mobile and power restricted and thus, disconnection and network partitioning occur frequently. In addition, many MANET database transactions have time constraints. In this paper, a Data REplication technique for real-time Ad-hoc Mobile databases (DREAM) is proposed that addresses all those issues. It improves data accessibility while considering the issue of energy limitation by replicating hot data items at servers that have higher remaining power. It addresses disconnection and network partitioning by introducing new data and transaction types and by considering the stability of wireless link. It handles the real-time transaction issue by replicating data items that are accessed frequently by firm transactions before those accessed frequently by soft transactions. DREAM is prototyped on laptops and PDAs and compared with two existing replication techniques using a military database application. The results show that DREAM performs the best in terms of percentage of successfully executed transactions, servers’ and clients’ energy consumption, and balance of energy consumption distribution among servers

Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity

International audienceWe present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of Objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the Object Clustering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance

A Vision of a Decisional Model for Re-optimizing Query Execution Plans Based on Machine Learning Techniques

International audienceMany of the existing cloud database query optimization algorithms target reducing the monetary cost paid to cloud service providers in addition to query response time. These query optimization algorithms rely on an accurate cost estimation so that the optimal query execution plan (QEP) is selected. The cloud environment is dynamic, meaning the hardware configuration, data usage, and workload allocations are continuously changing. These dynamic changes make an accurate query cost estimation difficult to obtain. Concurrently, the query execution plan must be adjusted automatically to address these changes. In order to optimize the QEP with a more accurate cost estimation, the query needs to be optimized multiple times during execution. On top of this, the most updated estimation should be used for each optimization. However, issues arise when deciding to pause the execution for minimum overhead. In this paper, we present our vision of a method that uses machine learning techniques to predict the best timings for optimization during execution

High-performance online spatial and temporal aggregations on multi-core CPUs and many-core GPUs, in:

a b s t r a c t With the increasing availability of locating and navigation technologies on portable wireless devices, huge amounts of location data are being captured at ever growing rates. Spatial and temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale ubiquitous urban sensing data play an important role in understanding urban dynamics and facilitating decision making. Unfortunately, existing spatial, temporal and spatiotemporal OLAP techniques are mostly based on traditional computing frameworks, i.e., disk-resident systems on uniprocessors based on serial algorithms, which makes them incapable of handling largescale data on parallel hardware architectures that have already been equipped with commodity computers. In this study, we report our designs, implementations and experiments on developing a data management platform and a set of parallel techniques to support highperformance online spatial and temporal aggregations on multi-core CPUs and many-core Graphics Processing Units (GPUs). Our experiment results show that we are able to spatially associate nearly 170 million taxi pickup location points with their nearest street segments among 147,011 candidates in about 5-25 s on both an Nvidia Quadro 6000 GPU device and dual Intel Xeon E5405 quad-core CPUs when their Vector Processing Units (VPUs) are utilized for computing intensive tasks. After spatially associating points with road segments, spatial, temporal and spatiotemporal aggregations are reduced to relational aggregations and can be processed in the order of a fraction of a second on both GPUs and multi-core CPUs. In addition to demonstrating the feasibility of building a high-performance OLAP system for processing large-scale taxi trip data for real-time, interactive data explorations, our work also opens the paths to achieving even higher OLAP query efficiency for large-scale applications through integrating domain-specific data management platforms, novel parallel data structures and algorithm designs, and hardware architecture friendly implementations

Performance Comparison of Scheduling Techniques to Manage Transactions for Real-Time Mobile Databases in Ad Hoc Networks

A Mobile Ad-hoc Network (MANET) ¡s an autcnomous system of mobile hosts (MHs) with similar transmission power and computation capabilities that communicate over relatively bandwidth constrained wireless links. Applications such as emergency/rescue operations, conferences/meetings/lectures, dísaster refief efforts, bluetooth (Personal Area Network} and military networks can be conceived as applications of MAIMET due to the fact that they cannot rely on centralized and organized connectivity. In these environment transactions are time-crltical and require to be executed not only correclly but also within their deadlines, that is, the user that submit a transaction would like it to be completed before a certain time in the future. This study focuses on the comparison of four scheduling techniques based on the policy of assigning priorities to transactions on the system. The techniques are: First Come First Serve (FCFS) [1,2], Earliest Deadline (ED) [1,2,5], Least Slack (LS) [1,2,8] and Least Slack Mobile (LSM) proposed in [3] where some modifications to the Least Slack Technique with respect to energy constraints, disoonnection and transaction type (firnVsoft) are considered. Applying these modifications to Earliest Deadline, the performance of the system will be evaluated to measure the percentage of transaction missing deadlines and the total energy consumption in the mobile hosts. The performance evaluation of the techniques will be carried out by means of simulation. The simulation model is implemented using Visual Slam/Awesim [7]

A Prototype for Translating XQuery Expressions into XSLT Stylesheets

Abstract. The need for a user-friendly query language becomes increasingly important since the introduction of XML. The W3C developed XQuery for the purpose of querying XML data, but XQuery is not available in every tool. Because of historical reasons, many tools only support processing XSLT stylesheets. It is desirable to use tools with XQuery, the design goals of which are, among other goals, to be more human readable and to be less error-prone than XSLT. Instead of implementing XQuery support for every tool, we propose to use an XQuery to XSLT translator. Following this idea, XQuery will be available for all tools, which currently support XSLT stylesheets. In this paper, we propose a translator which transforms XQuery expressions into XSLT stylesheets and we analyze the performance of the translation and XSLT processing in comparison to native XQuery processing

06431 Working Group Summary: Atomicity in Mobile Networks

We introduce different mobile network applications and show to which degree the concept of database transactions is required within the applications. We show properties of transaction processing and explain which properties are important for each of the mobile applications. Furthermore, we discuss open questions regarding transaction processing in mobile networks and identify open problems for further research

Simulation of Main Memory Database Recovery

In a main memory database (MMDB), the primary copy of the database may reside permanently in a volatile memory. When a system failure occurs, the database must be reloaded efficiently from archive memory into main memory. This paper presents four different reload schemes and the simulation models constructed to compare the algorithms. Simulation results indicate that the reload scheme based on freguency of data access gives the best overall performance in terms of transaction response time and system throughput.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems

International audienceCurrent mobile cloud database systems are widespread and require special considerations for mobile devices. Although many systems rely on numerous metrics for use and optimization, few systems leverage metrics for decisional cache replacement on the mobile device. In this paper we introduce the Lowest Scored Replacement (LSR) policy-a novel cache replacement policy based on a predefined score which leverages contextual mobile data and user preferences for decisional replacement. We show an implementation of the policy using our previously proposed MOCCAD-Cache as our decisional semantic cache and our Normalized Weighted Sum Algorithm (NWSA) as a score basis. Our score normalization is based on the factors of query response time, energy spent on mobile device, and monetary cost to be paid to a cloud provider. We then demonstrate a relevant scenario for LSR, where it excels in comparison to the Least Recently Used (LRU) and Least Frequently Used (LFU) cache replacement policies