Scalable and Efficient Spatial-Aware Parallelization Strategies for Multimedia Retrieval

In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of computer users (Yang & Hurson, 2006). The integration of wireless communication, pervasive computing, and ubiquitous data processing with multimedia database systems has enabled the connection and fusion of distributed multimedia data sources. In addition, the emerging applications, such as smart classroom, digital library, habitat/environment surveillance, traffic monitoring, and battlefield sensing, have provided increasing motivation for conducting research on multimedia content representation, data delivery and dissemination, data fusion and analysis, and contentbased retrieval. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought—video, audio, and images (Karpouzis, Raouzaiou, Tzouveli, Iaonnou, & Kollias, 2003; Liu, Bao, Yu, & Xu, 2005; Yang & Hurson, 2005). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations such as imprecise query or content-based retrieval. For instance, the image database systems may accept an example picture and return the most similar images of the example (Cox, Miller, & Minka, 2000; Hsu, Chua, & Pung, 2000; Huang, Chang, & Huang, 2003). However, the conveniences of multimedia applications come with challenges to the existing data management schemes: • Efficiency: Multimedia applications generally require more resources; however, the storage space and processing power are limited in many practical systems, for example, mobile devices and wireless networks (Yang & Hurson, 2005). Due to the large data volume and complicated operations of multimedia applications, new methods are needed to facilitate efficient representation, retrieval, and processing of multimedia data while considering the technical constraints. • Semantic Gap: There is a gap between user perception of multimedia entities and physical representation/access mechanism of multimedia data. Users often browse and desire to access multimedia data at the object level (“entities” such as human beings, animals, or buildings). However, the existing multimedia retrieval systems tend to access multimedia data based on their lower-level features (“characteristics” such as color patterns and textures), with little regard to combining these features into data objects. This representation gap often leads to higher processing cost and unexpected retrieval results. The representation of multimedia data according to human’s perspective is one of the focuses in recent research activities; however, few existing systems provide automated identification or classification of objects from general multimedia collections. • Heterogeneity: The collections of multimedia data are often diverse and poorly indexed. In a distributed environment, because of the autonomy and heterogeneity of data sources, multimedia data objects are often represented in heterogeneous formats. The difference in data formats further leads to the difficulty of incorporating multimedia data objects under a unique indexing framework. • Semantic Unawareness: The present research on content-based multimedia retrieval is based on feature vectors—features are extracted from audio/video streams or image pixels, empirically or heuristically, and combined into vectors according to the application criteria. Because of the application-specific multimedia formats, the feature-based paradigm lacks scalability and accuracy.

This paper proposes an efficient indexing structure for CBMR (Content-Based Multimedia Retrieval), called HBI (Hierarchical Bitmap Index), in which each object is represented as a bitmap of size 2 · d · l bits, where d is the number of dimensions of object's feature vector and l is the number of bitmaps. In this bitmap representation, the feature (or attribute) value of object at each dimension is represented with a set of two bits each of which indicates whether it is relatively high ('11'), low ('00'), or neither ('01') compared to the feature values of other objects at a hierarchical organized interval. Using these compact representations of feature vectors, a lot of irrelevant objects could be quickly filtered-out by a couple of simple XOR operations, and it helps to reduce the filtering process of similarity search in high-dimensional data space. It also presents an optimization algorithm, called FQD (Filtering by Query Difference), for the similarity search with relevance feedback that reuses the previously calculated distances between the original query and all objects in the database when filtering the irrelevant objects in the successive search with modified query. It helps to further reduce the search time of CBMR with relevance feedback. Experimental results show that the similarity search using HBI is about 2 ~ 3 times faster than VA-File while guaranteeing the exact solutions, and FQD for relevance feedback helps to further reduce the elapsed time of successive similarity search compared to the one for the first search.

BackgroundSimilaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. ResultsTo bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep.ConclusionsEfficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

As the size of multimedia database grows, it becomes impractical to manually annotate all contents and attributes of the media, and the difficulty in finding desired information increases. To copy with these challenges, content-based multimedia retrieval systems have been developed for various applications. The chapter not only provides a conceptual architecture for the design of content-based retrieval system, but also discusses essential components of retrieval system and their research issues, including feature extraction and representation, dimension reduction of feature vector, indexing, and query specifications. As content-based multimedia retrieval is a young research field and there exists many challenging research problems, this chapter also addresses several research issues for the future research.

Content-based multimedia information retrieval is never a trivial task even with state-of-the-art approaches. Its mandatory challenge, called “semantic gap,” requires much more understanding of the way human perceive things (i.e., visual and auditory information). Computer scientists have spent thousands of hours seeking optimal solutions, only ended up falling in the bound of this gap for both visual and spoken contexts. While an over-the-gap approach is unreachable, we insist on assembling current viable techniques from both contexts, aligned with a domain concept base (i.e., an ontology), to construct an info service for the retrieval of agricultural multimedia information. The development process spans over three packages: (1) building a Vietnamese agricultural thesaurus; (2) crafting a visual-auditory intertwined search engine; and (3) system deployment as an info service. We spring our the thesaurus in 2 sub-boughs: the aquaculture ontology consists of 3455 concepts and 5396 terms, with 28 relationships, covering about 2200 fish species and their related terms; and the plant production ontology comprises of 3437 concepts and 6874 terms, with 5 relationships, covering farming, plant production, pests, etc. These ontologies serve as a global linkage between keywords, visual, and spoken features, as well as providing the reinforcement for the system performances (e.g., through query expansion, knowledge indexing…). On the other hand, constructing a visual-auditory intertwined search engine is a bit trickier. Automatic transcriptions of audio channels are marked as the anchor points for the collection of visual features. These features, in turn, got clustered based on the referenced thesauri, and ultimately tracking out missing info induced by the speech recognizer’s word error rates. This compensation technique bought us back 14 % of loss recall and an increase of 9 % accuracy over the baseline system. Finally, wrapping the retrieval system as an info service guarantees its practical deployment, asour target audiences are the majority of farmers in developing countries who are unable to reach modern farming information and knowledge.

In recent years, multimedia content-based retrieval has become an important research problem. In order to provide effective and also efficient access to relevant data stored in large (often distributed) digital repositories, advanced software tools are necessary. Content-based retrieval works on the idea of abstracting the contents of an object, for example color or shape in the case of images, by so-called features-features are typically points in a high-dimensional vector space. Instead of determining the similarity of two objects based on their raw data, only the much smaller feature representations are used to estimate the objects' similarity. Given a reference (query) object represented by its features, similarity predicates are defined to retrieve a specific number of best cases or all objects satisfying a (distance) constraint. In this respect, we can distinguish between similarity range and nearest neighbor (NN) queries.

Many studies have been conducted on seeking an efficient solution for graph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Description Framework data management. All prior work assumes that the underlying data is deterministic. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and for privacy-preserving purposes. Therefore, in this paper, we study similarity graph containment search on large uncertain graph databases. Similarity graph containment search consists of subgraph similarity search and supergraph similarity search. Different from previous works assuming that edges in an uncertain graph are independent of each other, we study uncertain graphs where edges' occurrences are correlated. We formally prove that subgraph or supergraph similarity search over uncertain graphs is $$\#$&#P-hard; thus, we employ a filter-and-verify framework to speed up these two queries. For the subgraph similarity query, in the filtering phase, we develop tight lower and upper bounds of subgraph similarity probability based on a probabilistic matrix index (PMI). PMI is composed of discriminative subgraph features associated with tight lower and upper bounds of subgraph isomorphism probability. Based on PMI, we can filter out a large number of uncertain graphs and maximize the pruning capability. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. For the supergraph similarity query, in the filtering phase, we propose two pruning algorithms, one lightweight and the other strong, based on maximal common subgraphs of query graph and data graph. We run the two pruning algorithms against a probabilistic index that consists of powerful graph features. In the verification, we design an approximate algorithm based on the Horvitz---Thompson estimator to fast validate the remaining candidates. The efficiencies of our proposed solutions to the subgraph and supergraph similarity search have been verified through extensive experiments on real uncertain graph datasets.

Recently, use of parallel machines in reservoir simulation has received considerable attention from the petroleum industry. This paper presents parallelization of a 3D compositional, equation-of-state reservoir simulator on the CM-5 and KSR-1. To the best of our knowledge, this is the first time that the parallelization of a compositional reservoir simulator has been performed on both the CM-5 and KSR-1. For new users of the CM-5 machines, the software and hardware of CM-5 architecture is presented, as well as details of the parallelization techniques. For example, domain decomposition, I/O's, phase equilibrium computations, and well model are described. The parallelism techniques on the KSR-1 are presented with the emphasis on the porting of the phase equilibrium calculation. The performance of each machine is evaluated by showing the speedup on different sets of processing nodes. Two test problems were used to explore the capability of the parallelized version of the code; one is a waterflood problem and the other is a CO2 multiple contact miscible flood, both in a West Texas oil field. These field problems were run on 1, 2, 4, 8, 16, and 32 processors to get insight into the locations of communication bottlenecks, generally occurring in the programming with distributed memory machines. The problems of latency and bandwidth which are associated with communication efficiency of the CM-5 are also addressed.

Summary form only given. After 15 years of extensive research efforts, multimedia retrieval has final come to its prime time when everything becomes accessible on the Web. However, Web search both provides a new paradigm and poses challenge to multimedia retrieval research. It calls for a rethinking of the traditional content-based approaches, especially in how to make use the massive but noisy meta-data associated with Web pages and links. In this talk, we will first review some familiar approaches in content-based multimedia retrieval and examine their limits. We will then present a few new efforts in web multimedia search to illustrate some new thoughts in this space.

Among elliptic boundary value problems, the class of problems with separable variables can be solved fast and directly. Elliptic problems with separable variables usually assume that the computational domains are simple e.g., rectangle or circle, and constant coefficients [1]. This kind of problems can serve to generate preconditioners in iterative procedures for more complex methods. They can also be used in low-accuracy models similar to the ones used in Numerical Weather Simulations. A straightforward implementation of solvers for such problems makes them considered as too simple to pay much attention to them. For instance, NETLIB* contains the codes of various solvers for problems with separable variables. We are not aware if such solvers are provided as separate solvers in other software packages except NETLIB Fishpack* and Intel$\textcircled{\tiny{\,R}}$ Math Kernel Library (Intel$\textcircled{\tiny{\,R}}$ MKL) together with Intel$\textcircled{\tiny{\,R}}$ Cluster Poisson Solver Library (Intel$\textcircled{\tiny{\,R}}$ CPSL). The problems with the separable variables can be suboptimal in the sense that they require slightly more arithmetic operations (up to logarithmic factor) than the number of unknowns to compute the solution to the problem. This statement is true if, for example, the sizes of the discretized problems are powers of 2. Computational Mathematics suggests that we take into consideration not only arithmetic operations, but also the cost of memory operations as well. Modern computers can perform computations at a very high speed, while lacking the ability to deliver data to the computational units. Keeping in mind that a memory operation can easily be dozen to hundred times slower than an arithmetic one, a computationally optimal algorithm could compute the solution slower than memory optimal algorithm. The recent developments in processor industry resulted in multicore processors become standard processors not only for powerful clusters, but also for home computers and laptops. Therefore, the algorithm can also be non-optimal from the parallelization point of view. Optimality here can be understood in terms of the number of synchronization points and/or in terms of the amount of data that needs to be transferred between different cores/processors. In summary, the modern computational algorithm should focus on 3 key aspects, namely, parallelization, memory operations, and arithmetic costs. The purpose of this paper is to demonstrate on a simple 2D problem with separable variables that taking into account modern model the solution can be computed efficiently and fast. This would also help developers to compute solution to the problem with separable variables really negligible with respect to other computations. To complete our goal, we will evaluate software provided by Intel Corporation. In particular, we focus on the comparison (where possible) of NETLIB Fishpack* and Intel$\textcircled{\tiny{\,R}}$ CPSL provided at [2] as well as Intel$\textcircled{\tiny{\,R}}$ MKL provided at [3]. This paper is organized as follows. First we are solving a 2D Helmholtz problem using single precision on a shared memory machine with two 4-Core Intel$\textcircled{\tiny{\,R}}$ Xeon$\textcircled{\tiny{\,R}}$ Processors E5462. Next we do the same but use double precision arithmetic. Lastly we use double precision on a distributed memory machine using MPI.

A critical issue in achieving the performance of data parallel programs is how to efficiently decompose data across processors. On distributed-memory machines, a good data decomposition should increase processor workload balance and reduce interprocessor communication. Data decomposition consists of data distribution and data alignment. In this paper, we propose a trapezoid data distribution pattern and new data alignment algorithms using alignment graph (AG). Our AG-based alignment framework is unique from other related work because it takes advantage of the effect of optimal expression evaluation with regard to multiple assignment statements.

Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.

Range query processing on single and multi GPU environments

Similarity searches using topological descriptors have proved extremely useful in aiding large-scale screening. In this paper we describe the geometric atom pair, the 3D analog of the topological atom pair descriptor (Carhart et al. J. Chem. Inf. Comput. Sci. 1985, 25, 64−73). We show the results of geometric similarity searches using the CONCORD-build structures of typical small druglike molecules as probes. The database to be searched is a 3D version of the Derwent Standard Drug File that contains an average of 10 explicit conformations per compound. Using objective criteria for determining how good a descriptor is in selecting active compounds from large databases, we compare the results using the geometric versus the topological atom pair. We find that geometric and topological atom pairs are about equally effective in selecting active compounds from large databases. How the two types of descriptors rank active compounds is generally about the same as well, but occasionally active compounds will be se...

Content-based multimedia information retrieval is an interesting but difficult area of research. Current approaches include the use of color, texture, and shape information. This paper proposes a novel approach to content-based image retrieval. In our retrieval system, an image is represented by a set of color histogram and edge histogram descriptors. The histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. Finally, the overall similarity is computed as a weighted combination of image similarity measures incorporating all features. Our proposed retrieval approach demonstrates a promising performance for an image database including 766 general-purpose images. Effectiveness is documented by experimental results.