We introduce REDO-SLAM, a robust visual SLAM system specifically designed for dynamic environments, which addresses a major drawback in many existing SLAM techniques. Our approach optimally selects a model for segmenting dynamic objects by combining a computationally efficient optical flow neural network with segmentation models, reducing the number of frames needed for applying resourceintensive semantic segmentation models. By using optical flow values, we can predict object masks, enabling the system to effectively manage dynamic objects. We also employ deep reinforcement learning (DRL) to determine the most appropriate frames for accurate mask prediction based on motion-aware rewards. We assessed our method using the TUM and Kitti datasets, showcasing its outstanding performance in dynamic settings. Our method significantly lowers computation time and maintains satisfactory performance in obtaining keyframe trajectories. In comparison to recent visual SLAM systems, our approach is more scalable, efficient, and resilient.

This paper presents a novel surface registration technique using the spectrum of the shapes, which can facilitate accurate localization and visualization of non-isometric deformations of the surfaces. In order to register two surfaces, we map both eigenvalues and eigenvectors of the Laplace-Beltrami of the shapes through optimizing an energy function. The function is defined by the integration of a smoothness term to align the eigenvalues and a distance term between the eigenvectors at feature points to align the eigenvectors. The feature points are generated using the static points of certain eigenvectors of the surfaces. By using both the eigenvalues and the eigenvectors on these feature points, the computational efficiency is improved considerably without losing the accuracy in comparison to the approaches that use the eigenvectors for all vertices. In our technique, the variation of the shape is expressed using a scale function defined at each vertex. Consequently, the total energy function to align the two given surfaces can be defined using the linear interpolation of the scale function derivatives. Through the optimization of the energy function, the scale function can be solved and the alignment is achieved. After the alignment, the eigenvectors can be employed to calculate the point-to-point correspondence of the surfaces. Therefore, the proposed method can accurately define the displacement of the vertices. We evaluate our method by conducting experiments on synthetic and real data using hippocampus, heart, and hand models. We also compare our method with non-rigid Iterative Closest Point (ICP) and a similar spectrum-based methods. These experiments demonstrate the advantages and accuracy of our method.

In this paper, we present a web-based medical image processing scientific workflow system called DATAVIEW. This platform is implemented to satisfy the need of physicians to process their data by computer scientists and therefore, overcome the problem of delay for communicating between these two groups and speed up the processing which is very important in emergency conditions. For employing this workflow system, clinicians do not require to install any software tools and can create, save, share, reuse, and run their workflows only using a web browser without knowing the implementation details of the service. For employing the DATAVIEW workflow system for the medical image processing purpose, we extend this workflow system by integrating medical image software tools to this platform which is one of the challenges of this research. Also, in order to provide processing the data in parallel, we integrate high-end computing resource like grids to the system to speed up the processing, which is another challenge for this work. This is very important in medical imaging field because some software tools are very time consuming and in the urgent time, the physicians need to process multiple patients in parallel and get the results fast. As a case study, we choose epilepsy, which is one of the most common brain disorders.

Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must be taken into consideration in the ranking and evaluation phases in order to legitimately apply a feature ranking method. In a comparison study, a Performance Index (PI) is proposed that takes into account both the number of features and the number of samples involved in the classification.

Provenance has become increasingly important in scientific workflows and services computing to capture the derivation history of a data product, including the original data sources, intermediate data products, and the steps that were applied to produce the data product. In many cases, both scientific results and the used protocol are sensitive and effective access control mechanisms are essential to protect their confidentiality. In this paper, we propose: 1) a formal scientific workflow provenance model as the basis for querying and access control for workflow provenance; 2) a security model for fine-grained access control for multilevel provenance and an algorithm for the derivation of a full security specification based on inheritance, overriding, and conflict resolution; 3) a formalization of the notion of security views and an algorithm for security view derivation; and 4) a formalization of the notion of secure abstraction views and an algorithm for its computation. A prototype called SecProv has been developed, and experiments show the effectiveness and efficiency of our approach.

Scientific workflows have recently emerged as a new paradigm for scientists to formalize and structure complex and distributed scientific processes to enable and accelerate many scientific discoveries. In contrast to business workflows, which are typically control flow oriented, scientific workflows tend to be dataflow oriented, introducing a new set of requirements for system development. These requirements demand a new architectural design for scientific workflow management systems (SWFMSs). Although several SWFMSs have been developed that provide much experience for future research and development, a study from an architectural perspective is still missing. The main contributions of this paper are: 1) based on a comprehensive survey of the literature and identification of key requirements for SWFMSs, we propose the first reference architecture for SWFMSs; 2) according to the reference architecture, we further propose a service-oriented architecture for View (a VIsual sciEntific Workflow management system); 3) we implemented View to validate the feasibility of the proposed architectures; and 4) we present a View-based scientific workflow application system (SWFAS), called FiberFlow, to showcase the application of our View system.

The Second International Workshop on the Many Faces of Multimedia Semantics (MS'08) is the second such workshop held in conjunction with the ACM International Multimedia Conference. This workshop provides a forum for practitioners, artists, and scientists to exchange ideas on how to develop adaptive media retrieval techniques which satisfy individual user needs.

Provenance, the metadata that pertains to the derivation history of a data product, has become increasingly important in scientific workflow environments. In many cases, both data products and their provenance can be sensitive and effective access control mechanisms are essential to protect their confidentiality. In this paper, we propose i) a formalization of scientific workflow provenance as the basis for querying and access control; ii) a security specification mechanism for provenance at various granularity levels and the derivation of a full security specification based on inheritance, overriding, and conflict resolution rules; iii) a formalization of security views that are derived from a scientific workflow run provenance for different roles of users; and iv) a framework that integrates abstraction views and security views such that a user can examine provenance at different abstraction levels while respecting the security policy prescribed for her. We have developed the SecProv prototype to validate the effectiveness of our approach.

Relational technology has shown to be very useful for scalable Semantic Web data management. Numerous researchers have proposed to use RDBMSs to store and query voluminous RDF data using SQL and RDF query languages. In this article, we study how RDF queries with the socalled well-designed graph patterns and nested optional patterns can be efficiently evaluated in an RDBMS. We propose to extend relational databases with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional patterns of well-designed graph patterns. We design three efficient algorithms to implement the new operator in relational databases: (1) nested-loops NOJ algorithm (NL-NOJ); (2) sortmerge NOJ algorithm (SM-NOJ); and (3) simple hash NOJ algorithm (SH-NOJ). Based on a real-life RDF dataset, we demonstrate the efficiency of our algorithms by comparing them with the corresponding left outer join implementations and explore the effect of join selectivity on the performance of our algorithms.

Provenance management has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. This paper proposes an approach to provenance management that seamlessly integrates the interoperability, extensibility, and reasoning advantages of semantic Web technologies with the storage and querying power of an RDBMS. Specifically, we propose: i) two schema mapping algorithms to map an arbitrary OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) two efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable.

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively.

In this paper we describe a socially-based game platform, the Urbarium, developed using XNA, Torque, and Muppets that will support learning of concepts for the laboratory components of the IEEE/ACM CS1 and CS2 programming courses. The Urbarium's narrative will span three different stages of social development in a real world location, the Valley of Oaxaca, Mexico. These stages will correspond to hunter-gatherer economies, tribal economies, and state organizations. The students interact with each other over a local-area network. By solving problems, students collect code segments, and exchange these segments to complete components. Completed components provide enhanced rewards to students on future moves. The idea is to support a net-centric approach to software development and interaction among students in the lab. We feel that this will support student learning within a network environment by weaving a social fabric of cooperation into the code development process early on in a students' career

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.

In this paper, we present a novel graph theoretic approach to the problem of document-word co-clustering. In our approach, documents and words are modeled as the two vertices of a bipartite graph. We then propose isoperimetric co-clustering algorithm (ICA) - a new method for partitioning the document-word bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over spectral approach in terms of the quality, efficiency and stability in partitioning the document-word bipartite graph.

In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.

We present a new approach to organize an image database by finding a semantic structure interactively based on multi-user relevance feedback. By treating user relevance feedbacks as weak classifiers and combining them together, we are able to capture the categories in the users' mind and build a semantic structure in the image database. Experiments performed on an image database consisting of general purpose images demonstrate that our system outperforms some of the other conventional methods

In this paper, we propose a technique to retrieve the images using the 'search by similarity' method with the help of multimodal keywords. Multimodal keywords consist of low-level MPEG-7 color descriptors and textual keywords. The visual keywords and textual keywords are combined together and the image collection is represented as a matrix, which is similar in representation to a term-document matrix. Using LSI (latent semantic indexing), we demonstrate that the visual keywords, when combined with textual keywords can improve the retrieval results to a great degree

BACKGROUND: Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets. DESCRIPTION: Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at http://proteome.wayne.edu/PIMdb.html CONCLUSION: The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.

Relevance feedback and semantic retrieval have received extensive attention recently in the computer vision community. In this article, we present a semantic image query system with integrated feedback mechanism. Our system has two major components: the low-level feature space and the semantic space. In the low-level feature space, images are described by multidimensional vectors and are clustered based on the similarity of their contents. In the semantic space, the relationship among keywords is captured by a semantic hierarchy built by the aid of WordNet. Based on our system architecture, we propose a novel feedback solution for semantic retrieval called semantic feedback, which allows our system to interact with users directly at the semantic level. The short-term and long-term learning process of the semantic feedback substantially improves the image retrieval and annotation performance of our system. We demonstrate the effectiveness of our approach with experiments using 5,000 images from Corel database. The significant contribution of this article is in the scenario of having a relatively small training data set compared to the testing data set. Some of the previous work in the same direction has chosen a very large training set and a very small testing set. Clearly, the problem that we try to solve is more realistic and challenging.

In this paper, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. We then build a Bayesian model to solve the classification problem. To preserve the in-variation in the training data and reduce the noises, we also propose to estimate the class conditional probabilities in the feature subspace constructed by complement components analysis (CCA). We demonstrate the effectiveness of our approach through experiments in terms of annotation precision and recall.

This is the first paper that proposes automatic image annotation using the semantics of XML. In this paper, we propose XPRM-XML path based relevance model for automatic image annotation. Our experimental results show that the proposed model has considerable advantage over single word annotations in performing automatic semantic annotation.

In this paper, we propose an interactive image annotation system. The proposed system has two connected components, the low-level feature space and the semantic space. Experiments show that our system is able to provide accurate annotation for images by learning the connections between the two spaces through statistical modelling, natural language processing, and users' interaction.

In this paper we present a semantic image retrieval system with integrated feedback mechanism. In our system, we propose a novel feedback solution for semantic retrieval: semantic feedback, which allows our system to interact with users directly at the semantic level. The learning process of the semantic feedback substantially improves the image retrieval performance of the proposed system. We demonstrate the effectiveness of our approach with experiments using 5,000 images from Corel database.

In an annotated image database, keywords are usually associated with images instead of individual regions, which poses a major challenge for any region based image annotation algorithm. In this paper, we propose to learn the correspondence between image regions and keywords through Multiple-Instance Learning (MIL). After a representative image region has been learned for a given keyword, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. The classification problem is then addressed using the Bayesian framework. The proposed image annotation method is evaluated on an image database with 5,000 images.

We examine the efficacy of using MPEG-7 in a native XML database environment for managing aerial image data. This application domain exhibits unique challenges in the management of image data which is continually changing, requiring continuous update of the schemas and methods for mining various geographic region properties. We show how MPEG-7 descriptors can be associated with aerial images and their derived features at each stage of the image insertion process, producing a multifaceted integrated representation, which can be used to give the database administrator an indication as to when this representation should be rebuilt, so as to result in improved retrieval behavior. In the absence of specialized indexes for inexact descriptor matching, we also show how MPEG-7 descriptors can be used to speed up this process.

The development of the Semantic Web, the next-generation Web, greatly relies on the availability of ontologies and powerful annotation tools. However, there is a lack of ontology-based annotation tools for linguistic multimedia data. Existing tools either lack ontology support or provide limited support for multimedia. To fill the gap, we present an ontology-based linguistic multimedia annotation tool, OntoELAN, which features: (1) the support for OWL ontologies; (2) the management of language profiles, which allow the user to choose a subset of ontological terms for annotation; (3) the management of ontological tiers, which can be annotated with language profile terms and, therefore, corresponding ontological terms; and (4) storing OntoELAN annotation documents in XML format based on multimedia and domain ontologies. To our best knowledge, OntoELAN is the first audio/video annotation tool in the linguistic domain that provides support for ontology-based annotation. It is expected that the availability of such a tool will greatly facilitate the creation of linguistic multimedia repositories as islands of the Semantic Web of language engineering.

BACKGROUND: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. RESULTS: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm. CONCLUSIONS: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

In this paper we use a modular neural network to predict the molecular functions of yeast proteins. To solve this class problem, our proposed approach decomposes the original problem into a set of solvable 2-class subproblems using class information. Each 2-class problem has a set of positive and negative data. The yeast data is not equally distributed in function classes and hinders the learning of each neural network. We adopt a sampling strategy that generates a set of new class data to the subordinate class in order to balance the positive and negative data set. In data preparation, the biological concept of "guilt-by-interaction" is used for covering possible interaction partners among proteins of known functions. The proposed framework has been tested as a predictive model of yeast protein functions where the data source is stored in a relational database. In the experiments, the proposed system shows an average accuracy of 91.0% in the test set.

We define several disclosure risk measures for microdata. We analyze disclosure risk based on the disclosure control techniques applied to initial microdata. Disclosure Control is the discipline concerned with the modification of data containing confidential information about individual entities, such as persons, households, businesses, etc. in order to prevent third parties working with these data from recognizing entities in the data and thereby disclosing information about these entities. In very broad terms, disclosure risk is the risk that a given form of disclosure will occur if a masked microdataset is released. Microdata represents a series of records, each record containing information on an individual unit. The disclosure risk measures presented in the paper are validated in our experiments.

In this paper, we describe the architecture of a protein interaction database and tools for manipulating Drosophila protein interaction data. The proposed system not only maintains interaction data collected by an experiment, but also associates the interaction data with valuable data from various genomic databases. The system inherits a layered-modular architecture by introducing a wrapper-mediator approach in order to solve the syntactic and semantic heterogeneity among multiple data sources. The component modules for wrapping and integrating the relevant data, querying the database, and visualizing the interaction data among proteins are discussed. The system wrapped the relevant data for 14,000 Drosophila proteins from 5 publicly accessible sources. A web-based query interface is developed to browse the database and a query result can be viewed as in a protein interaction map depicting functional pathways, complexes or networks. Protein interaction maps aid in understanding or predicting potential functions for uncharacterized proteins and in describing their functional networks in a biological context. We show that the proposed approach supports data association and data interoperability in a protein interaction database.

A principal problem facing users of scientific or engineering databases is that of finding the correct data, due to a lack of detailed knowledge about the structure or schema of the available data. This paper discusses an approach to the design and development of a web-based interface that incorporate a visual model to access database information. This interface links a VRML (Virtual Reality Modeling Language) world to a database, creating an accessible 3D query interface that eliminates the need to know database query language syntax, database structure, and VRML. The user views the virtual world and retrieves information from a connected database with the click of a button. The interface offers the ability to create dynamic 3D anchors and associates queries that relate to parts of a VRML world. The main advantage of this virtual design is the simplicity of data retrieval for the end user.

Introduces a spatial access method, the SB/sup +/-tree, that is based on the B/sup +/-tree structure. For each axis of the space, a set of indexing points is generated; an indexing point is created whenever a new minimum bounding rectilinear rectangle (MBR) begins or ends. These indexing points are then used to create an SB/sup +/-tree. We present algorithms using an SB/sup +/-tree for performing two spatial selection operators (window intersection and point containment), and one spatial join operator (region containment). Analytically, we show the improved performance of the window intersection operation using an SB/sup +/-tree over using existing paged-secondary memory spatial access structures such as the R/sup */-tree.

This paper presents a greedy algorithm to compute the best set of views with respect to a given set of queries and their frequencies, for multidimensional databases. Previous approaches have not taken frequency of queries into consideration. We show that our proposed algorithm determines a set of views with the most benefits compared to previous approaches.

Most of the research in the area of database query optimization has dealt with data that is either in main memory or on disk. Lately there has been growing interest in making tertiary memory part of an information system. The authors propose a model for a generic storage system and generic data placement techniques. Such a model provides one with a tool to study query performance and optimization techniques in tertiary device based systems by varying the different storage system, data placement, and system configuration parameters. They then demonstrate one use of the model by using it in a tertiary storage system setup to measure the cost of relational join algorithms.

This paper presents a spatial access method based on the B+ -tree structure. This new structure is called SB+ -tree, and should allow commercial databases an access method for spatial objects without major changes, since most commercial databases already support B+ -tree as an access method for text data. SB+ -tree is a hybrid of the existing spatial access methods. For each axis of the space, a set of indexing points is generated, where an indexing point is created whenever a new minimum bounding rectilinear rectangle (or MBR) begins or ends. The indexing points are then used to create an SB+ -tree. The number of SB+ -trees generated is dependent upon the number of dimensions of the approximation of the object, and not on the number of relations. We have developed an algorithm which uses SB+ -tree for performing spatial join between two spatial relations. Through simulation, we show that the performance of spatial join using SB+ -tree is more efficient than that of R+ -tree.

The proliferation of databases into many areaa has resulted in the need for fast and efficient techniques for query processing.Large databases take too much time to process, and time constrained queries force the query processor to -, ---l -----"r-CIICM '9S, Baitimom MD USA

Much of the data that is stored in scientific databases is collected through experimentation. We propose a new interface to scientific databases: the SANDBOX: Scientists Accessing Necessary Data Based On eXperimentation. The SANDBOX is a virtual reality tool which allows an investigator to recreate the original experiment. The investigator places virtual instruments into a virtual reenactment of the original experiment and collects data from the scientific database in much the same way that the original data was collected. These instruments give visual and auditory feedback, allowing the user to browse through data of any type. We have implemented a prototype of the SANDBOX on NASA's FIFE scientific database using the CAVE virtual reality theatre.>

Two graph models are developed to determine the minimum required buffer size for achieving the theoretical lower bound on the number of disk accesses for performing relational joins. Here, the lower bound implies only one disk access per joining block or page. The first graph model is based on the block connectivity of the joining relations. Using this model, the problem of determining an ordered list of joining blocks that requires the smallest buffer is considered. It is shown that this problem as well as the problem of computing the least upper bound on the buffer size is NP-hard. The second graph model represents the page connectivity of the joining relations. It is shown that the problem of computing the least upper bound on the buffer size for the page connectivity model is also NP-hard. Heuristic procedures are presented for the page connectivity model and it is shown that the sequence obtained using the heuristics requires a near-optimal buffer size The authors also show the performance improvement of the proposed heuristics over the hybrid-has join algorithm for a wide range of join factors.>

Implementation of relational database queries by means of an index database machine is presented. The indexes are represented by a two-level structure. Performance analysis of this two-level index structure for reducing m-way join complexity is given. We also give a performance analysis of an algorithm for implementing m-way join on a proposed hardware architecture.