Abstract
Thanks to the explosion of the wireless technology, mobile environments are becoming the leading software platforms for extracting knowledge and interacting with enterprise information systems. Data and services availability at all times is the major benefit coming from such deployment scenario, but new research challenges pose serious limitations concerning data engineering issues. In fact, although if one can suppose that re-writing and re-adapting data structures, algorithms, and data reliability/dependability schemes is the natural way to support efficient data management on mobile environments, new issues and old limitations arise, particularly for what concerns with data availability and consistency in wireless network environments.
Top1. Introduction
Thanks to the explosion of the wireless technology, mobile environments are becoming the leading software platforms for extracting knowledge and interacting with enterprise information systems. Data and services availability at all times is the major benefit coming from such deployment scenario, but new research challenges pose serious limitations concerning data engineering issues. In fact, although if one can suppose that re-writing and re-adapting data structures, algorithms, and data reliability/dependability schemes is the natural way to support efficient data management on mobile environments, new issues and old limitations arise, particularly for what concerns with data availability and consistency in wireless network environments.
Furthermore, for a mobile application, design and deployment requirements strongly depend on its nature. For a service-oriented application, efficient service activation, and fast availability of data on which services run play a dominant role; on the contrary, for a data-intensive application, distributed storage, indexing, and querying are the most important (and problematic) issues. However, for any mobile application, location-aware servicesproviding (including data services) is the common deployment requirement to be satisfied. According to these considerations, every mobile software application should be able to provide data and services in any place and, above all, in any time.
Database Systems (DBS) and Data Warehouse Systems (DWS), which are usually built on the top of very large (and, very often, heterogeneous) data sources, are ready to gain innovative and important improvements by integrating the wireless environment into their application scope. In fact, these systems, which, without loss of generality, we can name as Data-Intensive Systems (DIS), can find in the mobile ones the natural front-end devices for making their impact on a large variety of applications (falling in even emerging contexts such as Business Intelligence (BI)) very successful. In fact, the development and, above all, the usage of many commercial DIS have shown that an on-site, just-in-time, fast, even approximate, computation (achievable through a wireless network infrastructure) can be often more profitable than a distant-in-time, data-intensive computation (achievable through a wired network infrastructure). In other words, mobile environments can reasonably be considered as a plus-value for data-intensive applications and systems.
In this respect, the data model assumes a critical role for DIS. As an example, the multidimensional data model has became a leading solution for DIS, thanks to its capability of representing and processing data according to a multidimensional and multi-resolution vision of data. This model has been adopted in several systems, with real benefits, such as e-banking systems, trading-on-line systems, basket analysis systems etc. OnLine Analytical Processing (OLAP) (Gray et al., 1997) is the leading technology for the multidimensional data model. Many of the above-mentioned systems integrate in their core layer an OLAP engine that offers storage and indexing functionalities, query and integration capabilities over multidimensional data.
Key Terms in this Chapter
Network Protocol: A set of rules establishing how data must be handled and transmitted throughout the network.
On-Line Analytical Processing (OLAP): A methodology for representing, managing and querying massive DW data according to multidimensional and multi-resolution abstractions of them.
Wireless Middleware: A collection of software components running in a wireless environment and realizing a distributed system among wireless devices. It implements a specific task or service, such as query services.
Data Compression: A collection of techniques aiming at reducing the size of massive data repositories (such as databases and data warehouses) and structures (such as data cubes) in order to mitigate access, process and query costs.
Data Reliability: A collection of techniques aiming at ensuring the consistency of data in distributed settings, such as mobile environments. In this specific case, data inconsistence is due to fault or unavailability of wireless protocols and devices.
Mobile Applications and Systems: Applications and systems incorporating in their scope the wireless environment, in combination or not with the traditional wired environment.
Location-Aware Services: Services whose providing depends on local characteristics such as geographical position and time.