An adaptive updating protocol for reducing moving object database workload

10.1007/s00778-011-0257-3VLDB Journal212265-28

An adaptive updating protocol for reducing moving object database workload

Proceedings of the VLDB Endowment31735-74

An Adaptive Updating Protocol for Reducing Moving Object Database Workload

In the last decade, spatio-temporal database research focuses on the design of effective and efficient indexing structures in support of location-based queries such as predictive range queries and nearest neighbor queries. While a variety of indexing techniques have been proposed to accelerate the processing of updates and queries, not much attention has been paid to the updating protocol, which is another important factor affecting system performance. In this paper, we propose a generic and adaptive updating protocol for moving object databases with less number of updating messages between the objects and database server, thereby reducing the overall workload of the system. In contrast to the approach adopted by most conventional moving object database systems where the exact locations and velocities last disclosed are used to predict their motions, we propose the concept of Spatio-Temporal Safe Region to approximate possible future locations. Spatio-temporal safe regions provide larger space of tolerance for moving objects, freeing them from location and velocity updates as long as the errors remain predictable in the database. To answer predictive queries accurately, the server is allowed to probe the latest status of some moving objects when their safe regions are inadequate in returning the exact query results. Spatio-temporal safe regions are calculated and optimized by the database server with two contradictory objectives: reducing update workload while guaranteeing query accuracy and efficiency. To achieve this, we propose a cost model that estimates the composition of active and passive updates based on historical motion records and query distribution. We have conducted extensive experiments to evaluate our proposal on a variety of popular indexing structures. The results confirm the viability, robustness, accuracy and efficiency of our proposed protocol

Forecasting in Database Systems

Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy