Data Engineering

A growing number of applications need access to video data stored in digital form on secondary storage devices (e.g., video-on-demand, multimedia messaging). As a result, video servers that are responsible for the storage and retrieval, at fixed rates, of hundreds of videos from disks are becoming increasingly important. Since video data tends to be voluminous, several disks are usually used in order to store the videos. A challenge is to devise schemes for the storage and retrieval of videos that distribute the workload evenly across disks, reduce the cost of the server and at the same time, provide good response times to client requests for video data. In this paper, we present schemes that are based on striping videos (fine-grained as well as coarse-grained) across disks in order to effectively utilize disk bandwidth. For the schemes, we show how an optimal-cost server architecture can be determined if data for a certain pre-specified number of videos is to be concurrently retrieved..

Data Engineering

As network connectivity has continued its explosive growth and as storage devices have become smaller, faster, and less expensive, the number of online digitized images has increased rapidly. Successful queries on large, heterogeneous image collections cannot rely on the use of text matching alone. In this paper we describe how we use image analysis in conjunction with an object relational database to provide both textual and content-based queries on a very large collection of digital images. We discuss the effects of feature computation, retrieval speed, and development issues on our feature storage strategy. 1 Introduction A recent search of the World Wide Web found 16 million pages containing the word "gif " and 3.2 million containing "jpeg" or "jpg." Many of these images have little or no associated text, and what text they do have is completely unstructured. Similarly, commercial image databases may contain hundreds of thousands of images with little useful text. To fully utilize..