Supporting Group-By and Pipelining in Bitmap-Enabled Query Processors
The Group-By operation is widely used in relational Data Warehouses for the aggregation and presentation of results according to user-defined criteria. Rearranging Group-By and join operations has been proposed as an optimization technique that reduces the size of the input relation. Pipelining is another optimization technique that promotes intra-query parallelism by transfering intermediate results to the next operation without materializing them. In Data Warehouses and environments in which exploratory on-line queries are common, pipelining can be used both for optimizing joins and for the reduction of response time by presenting partial results to the user. Efficient pipelining often depends on the order of the intermediate results. Order-dependent operations, such as Group-By, typically require the complete result set before ordering it, thereby making efficient pipelining impossible. In this paper we exploit bitmap indexing to implement Group-By in a manner that can be used for pipelining. Algorithms are presented for different assumptions about buffer availability and for different sort orders.

title: A Hyperrelational Approach to Integration and Manipulation of Data in Multidatabase Systems
abstract: The issue of interoperability among multiple autonomous databases has attracted a lot of attention from researchers in these years. The past research on this issue can be roughly divided into two main categories: the tightly-integrated approach that integrate databases by building an integrated schema and the loosely-integrated approach that achieves interoperability by using a multidatabase language. Most of the past efforts focused on the issues in the first approach. The problem with the first approach is, however, that it lacks a convenient representation of the integrated schema at the system level and a sound mathematical basis for data manipulation in a multidatabase system. In this paper, we propose to use hyperrelations as a powerful and succinct model for the global level representation of heterogeneous database schemas. A hyperrelation has the structure of a relation, but its contents are the schemas of the semantically equivalent local relations in the databases. With this representation, the metadata of the global database, local databases and the data of these databases are all representable by using the structure of a relation. The impact of such a representation is that all the elegant features of relational systems can be easily extended to multidatabase systems. A hyperrelational algebra is designed accordingly. This algebra is performed at the multidatabase systems (MDBS) level such that query transformation and optimization is supported on a sound mathematical basis. The major contributions of this paper include: (1) Local relations of various schemas (even though they retain information of the same semantics) can be systematically mapped to hyperrelations. As the structure of a hyperrelation is similar to that of a relation, data manipulation and management tasks (such as design of the global query language and the view mechanism) are greatly facilitated. (2) The hyperrelational algebra provides a sound basis for query transformation and optimization in a MDBS.

title: Using clustering and classification approaches in interactive retrieval
abstract: Satisfying non-trivial information needs involves collecting information from multiple resources, and synthesizing an answer that organizes that information. Traditional recall/precision-oriented information retrieval focuses on just one phase of that process: how to efficiently and effectively identify documents likely to be relevant to a specific, focused query. The TREC Interactive Track has as its goal the location of documents that pertain to different instances of a query topic, with no reward for duplicated coverage of topic instances. This task is similar to the task of organizing answer components into a complete answer. Clustering and classification are two mechanisms for organizing documents into groups. In this paper, we present an ongoing series of experiments that test the feasibility and effectiveness of using clustering and classification as an aid to instance retrieval and, ultimately, answer construction. Our results show that users prefer such structured presentations of candidate result set to a list-based approach. Assessment of the structured organizations based on the subjective judgement of the experiment subjects suggests that the structured organization can be more effective; however, assessment based on objective judgements shows mixed results. These results indicate that a full determination of the success of the approach depends on assessing the quality of the final answers generated by users, rather than on performance during the intermediate stages of answer construction.

title: Video transcoding by reducing spatial resolution
abstract: In network delivery of digital video, if the bandwidth required for a video is not available, the video has to be recoded at a reduced bit rate. It is highly desirable that the transcoding is carried out in real time while maintaining reasonable image quality. In this paper, we propose a fast approach to derive from an MPEG stream a new MPEG stream with half the spatial resolution. For the downsized video, we first generate from the original compressed video an improved estimate of the motion vectors. We then propose a compressed domain approach with data hiding to produce DCT residues by an open-loop method. The computational complexity is significantly lower than a number of previous approaches. Simulation suggests that our approach produces reasonable image quality.

title: Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting
abstract: Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.

title: Security evaluation for communication-friendly encryption of multimedia
abstract: This paper addresses the access control issues unique to multimedia, by using a joint signal processing and cryptographic approach to multimedia encryption. Based on three atomic encryption primitives, we present a systematic study on how to strategically integrate different atomic operations to build a video encryption system. We also propose a set of multimedia-specific security metrics to quantify the security against approximation attacks and to complement the existing notion of generic data security. The resulting system can provide superior performance to both generic encryption and its simple adaptation to video in terms of a joint consideration of security, bitrate overhead, and communication friendliness.

title: A Direct Method for Building Sparse Kernel Learning Algorithms
abstract: Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build sparse kernel learning algorithms by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting kernel machine is explicitly controlled while at the same time performance is kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the support vectom machine results in a concrete algorithm for building sparse large margin classifiers. These classifiers essentially find a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.