Data caching and hoarding provide the only means to support disconnected mobile operations. In the context of mobile database applications, data cached can take the form of a materialized view. In this paper, we present a mechanism or view holder within the fixed network, whose job is to maintain versions of the views that are required by a particular mobile host. These views are very likely to be a small and specialized portion of the information found within the various data sources and, therefore, versions can be dynamically maintained by the view holder without incurring huge storage requirements. In addition, the view holder will respond to mobile host's queries by communicating only the differences between versions. Thus, a view holder mediates and exports the views needed by a mobile host, and updates are computed and delivered in a flexible and batch manner. ffl Keywords: Mobile Computing, View Maintenance, Mobile Query Processing, Data Warehousing 1 Introduction Cur...

Recent developments in both hardware and software have made it worthwhile to consider embedding intelligence in storage to handle general purpose processing that can be offloaded from the hosts. In particular, low-cost processing power is now widely available and software can be made robust, secure and mobile. In this paper, we propose a general Smart Storage (SmartSTOR) architecture in which a processing unit that is coupled to one or more disks can be used to perform such offloaded processing. A major part of the paper is devoted to understanding the performance potential of the SmartSTOR architecture for decision support workloads since these workloads are increasingly important commercially and are known to be pushing the limits of current system designs. Our analysis suggests that there is a definite advantage in using fewer but more powerful processors, a result that bolsters the case for sharing a powerful processor among multiple disks. As for software architecture, we find that the offloading of database operations that involve only a single relation to the SmartSTORs is far less promising than the offloading of multiple-relation operations. In general, if embedding intelligence in storage is an inevitable architectural trend, we have to focus on developing parallel software systems that can effectively take advantage of the large number of processing units that will be in the system. 1

The requirements for storage space and computational power of largescale applications are increasing rapidly. Clusters seem to be the most attractive architecture for such applications, due to their low costs and high scalability. On the other hand, smart disk systems, with their large storage capacities and growing computational power are becoming increasingly popular. In this work, we compare the performance of these architectures with a single host-based system using representative queries from the Decision Support System (DSS) databases. We show how to implement individual database operations in the smart disk system and also show how to optimize the execution of the whole query by bundling frequently occurring operations together and executing the bundle in a single invocation. Besides decreasing the overall execution time, operation bundling also offers an easy-to-program and easy-to-use interface to access the data on smart disks. We also present a protocol for minimizing the communication time in the smart disk based system. To measure the response times, we have developed the DBsim, an accurate simulator which can simulate the database operations for the single host-based, cluster-based and smart disk based systems. Using this simulator, we illustrate that the smart disk architecture offers substantial benefits in terms of overall query execution times of the TPC-D benchmark suite. In particular, the average response time of the smart disk architecture for the representative queries from the TPC-D benchmark in our base configuration is 71 % smaller than the response time on the single host-based system and 4:2 % smaller than the response time on the fastest cluster architecture. We also demonstrate the effectiveness of the operation bundling. 1.

this paper, we present a detailed quantitative evaluation of a smart disk based architecture. To achieve this, we compare the performances of a smart disk system, two types of cluster systems and a single host system for whole database queries. The main contributions of this paper are as follows: ffl We present how a whole database query can be executed on a smart disk system. ffl We present and evaluate a method called operation bundling for reducing the execution time of the database queries in smart disk architecture

Smart disk systems with large storage capacities and growing computational power are becoming increasingly attractive. The idea is to perform parallel and filtering-type of data intensive computations on disks, close to data, thereby offloading the host processor and increasing the aggregate system power. In this

among mobile devices (i.e., a laptop vs. a pager) and the amount of information available from today's database environments and the Internet. To this end, this dissertation presents the development of customizable view maintenance services, called the View Holder approach, whose middleware mechanism within the fixed network dynamically maintains versions of the views so that to meet the data consistency and currency requirements of a particular mobile client. In a general form, a View Holder can support a community of mobile clients with common interests. The motivation for maintaining versions is to compensate for the data changes that occurred to the materialized views that were used during disconnection as well as to reduce the cost of wireless communication. In order to maintain these views, customized view maintenance is performed at the data sources by translating the mobile machine's request into a materialization program containing a triggering

Recent developments in both hardware and software have made it worthwhile to consider embedding intelligence in storage to handle general purpose processing that can be offloaded from the hosts. In particular, low-cost processing power is now widely available and software can be made robust, secure and mobile. In this paper, we propose a general Smart Storage (SmartSTOR) architecture in which a processing unit that is coupled to one or more disks can be used to perform such offloaded processing. A major part of the paper is devoted to understanding the performance potential of the SmartSTOR architecture for decision support workloads since these workloads are increasingly important commercially and are known to be pushing the limits of current system designs. Our analysis suggests that there is a definite advantage in using fewer but more powerful processors, a result that bolsters the case for sharing a powerful processor among multiple disks. As for software architecture, we find that the offloading of database operations that involve only a single relation to the SmartSTORs is far less promising than the offloading of multiple-relation operations. In general, if embedding intelligence in storage is an inevitable architectural trend, we have to focus on developing parallel software systems that can effectively take advantage of the large number of processing units that will be in the system.