Visual and multimedia analytics provides an emerging field of research combining strengths from information analytics, geospatial analytics, scientific analytics, statistical analytics, knowledge discovery, data management and knowledge representation, presentation, production and dissemination, cognition, perception, and interaction (Chen, Chiang and Storey, 2012). The aim is to gain insight into homogeneous, contradictory, and incomplete data through the combination of automatic analysis methods with human background knowledge and intuition. While the scope of visual analytics is broad, one principle that has emerged over the years is the need for visual analytics systems to leverage computational methods in data mining, knowledge discovery, and machine learning for large-scale data analysis. In these systems, the human operator works alongside the computational processes in an integrated fashion. Therefore, computing systems or services can sift through large amounts of data and identify the relevant information, while the human interactively explores the reduced data space to discover trends and patterns and make informed decisions. These two components operate in coordination, allowing for a continuous and cooperative analytical loop (Cybulski et al., 2015; Valdez et al., 2016). Top papers from Data 2018, Madrid, Spain and the best paper from FEMIB 2019 in Crete, Greece, have been invited. Through a robust and competitive review process, six papers have been selected.

Recent research trends exhibit a growing imbalance between the demands of tenants' software applications and the provisioning of hardware resources. Misalignment of demand and supply gradually hinders workloads from being efficiently mapped to fixed-sized server nodes in traditional data centers. The incurred resource holes not only lower infrastructure utilization but also cripple the capability of a data center for hosting large-sized workloads. This deficiency motivates the development of a new rack-wide architecture referred to as the composable system. The composable system transforms traditional server racks of static capacity into a dynamic compute platform. Specifically, this novel architecture aims to link up all compute components that are traditionally distributed on traditional server boards, such as central processing unit (CPU), random access memory (RAM), storage devices, and other application-specific processors. By doing so, a logically giant compute platform is created and this platform is more resistant against the variety of workload demands by breaking the resource boundaries among traditional server boards. In this paper, we introduce the concepts of this reconfigurable architecture and design a framework of the composable system for cloud data centers. We then develop mathematical models to describe the resource usage patterns on this platform and enumerate some types of workloads that commonly appear in data centers. From the simulations, we show that the composable system sustains nearly up to 1.6 times stronger workload intensity than that of traditional systems and it is insensitive to the distribution of workload demands. This demonstrates that this composable system is indeed an effective solution to support cloud data center services.

Optimal resource provisioning is essential for scalable big data analytics. However, it has been difficult to accurately forecast the resource requirements before the actual deployment of these applications as their resource requirements are heavily application and data dependent. This paper identifies the existence of effective memory resource requirements for most of the big data analytic applications running inside JVMs in distributed Spark environments. Provisioning memory less than the effective memory requirement may result in rapid deterioration of the application execution in terms of its total execution time. A machine learning-based prediction model is proposed in this paper to forecast the effective memory requirement of an application given its service level agreement. This model captures the memory consumption behavior of big data applications and the dynamics of memory utilization in a distributed cluster environment. With an accurate prediction of the effective memory requirement, it is shown that up to 60 percent savings of the memory resource is feasible if an execution time penalty of 10 percent is acceptable. The accuracy of the model is evaluated on a physical Spark cluster with 128 cores and 1TB of total memory. The experiment results show that the proposed solution can predict the minimum required memory size for given acceptable delays with high accuracy, even if the behavior of target applications is unknown during the training of the model.

We describe a novel content transcoding middleware for accessing military geospatial intelligence information in real-time. Intelligence information, including maps and location, category and properties of object targets, is adapted for various pervasive devices such as laptop, personal digital assistant (PDA), cellular phone, etc. The middleware is deployed as proxies on the Web using the IBM Websphere Transcoding Publisher (WTP) platform, which facilitates the middleware management. We developed several Java-based plug-ins and Extensible Stylesheet Language (XSL) stylesheets for content transcoding. A prototype has been established and real experiments have demonstrated the effectiveness of this novel middleware.

This article presents a method for adaptively representing multidimensional data cubes using wavelet view elements in order to more efficiently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of wavelet view elements. The view elements differ from traditional data cube cells in that they correspond to partial and residual aggregations of the data cube. The view elements provide highly granular building blocks for synthesizing the aggregated and range-aggregated views of the data cubes. We propose a strategy for selectively materializing alternative sets of view elements based on the patterns of access of views. We present a fast and optimal algorithm for selecting a non-expansive set of wavelet view elements that minimizes the average processing cost for supporting a population of queries of data cube views. We also present a greedy algorithm for allowing the selective materialization of a redundant set of view element sets which, for measured increases in storage capacity, further reduces processing costs. Experiments and analytic results show that the wavelet view element framework performs better in terms of lower processing and storage cost than previous methods that materialize and store redundant views for online analytical processing (OLAP).

Health activity monitoring (HAM) has received increasing attention due to the rapid advances of both hardware and software technologies and strong environmental and public health needs. In this paper, we describe the architecture and implementation of the Epi-SPIRE prototype, which is a novel health activity monitoring system that generates alerts from environmental, behavioral, and public health data sources. A model-based approach is used to develop disease and behavior models from multi-modal heterogeneous data sources. Furthermore, a model-based indexing technique has been developed to speed up the data access and retrieval. This system has been successfully applied to various genuine and simulated diseases outbreaks scenarios'.

Economics research has long recognized that bundling enables savings in production and transaction costs, promotes complementary among the bundle components and sorts consumers according to their valuations. Sellers employ market analysis and intelligence to extract the most surplus. In the age of electronic commerce with low product information access cost, buyers can take advantage of the benefits of bundling by performing dynamic composition of goods from multiple companies offering heterogeneous products and services. These goods, with the proper mix of sources and quantity, may offer additional discounts and benefits, which would not have risen should purchase decisions were made independently. A prominent example is packaged travel, which often involves air, hotel and car rentals. An optimal travel package search not only takes advantage of the lowest available prices of air, hotel and car rental individually but also exploits various discounts through business partnerships between service providers.Today's database infrastructure to support the search of dynamically bundled goods, however, is insufficient. The complex search operations involving cross join of many product categories with hundreds or thousands of offerings can be formulated as SQL queries. But executing these queries in a traditional database is inefficient. This paper proposes an I/O conscious, dynamic programming based algorithm for bundle search. The proposed algorithm finds the top-K combinations of goods abstracted by a linear relationship graph. Experimental results indicate that the proposed algorithm achieves more than two orders of magnitude speedup over cross join, and it is more than an order of-magnitude faster than the simple dynamic programming solution. The performance gap further widens as the number of product categories and the number of offerings within each category increase. This paper characterizes the computational and I/O complexity of the proposed algorithm and suggests extensions to search bundles with more complex relationships.

Nearest-neighbor search of high-dimensionality spaces is critical for many applications, such as content-based retrieval from multimedia databases, similarity search of patterns in data mining, and nearest-neighbor classification. Unfortunately, even with the aid of the commonly used indexing schemes, the performance of nearest-neighbor (NN) queries deteriorates rapidly with the number of dimensions. We propose a method, called Clustering with Singular Value Decomposition (CSVD), which supports efficient approximate processing of NN queries, while maintaining good precision-recall characteristics. CSVD groups homogeneous points into clusters and separately reduces the dimensionality of each cluster using SVD. Cluster selection for NN queries relies on a branch-and-bound algorithm and within-cluster searches can be performed with traditional or in-memory indexing methods. Experiments with texture vectors extracted from satellite images show that CSVD achieves significantly higher dimensionality reduction than plain SVD for the same normalized mean squared error (NMSE), which translates into a higher efficiency in processing approximate NN queries.

The Bayes point machine (BPM) has been demonstrated theoretically to have better learning ability than the support vector machine (SVM). We describe these two machines and tell how they differ. We empirically compare the performance of the BPM and the SVM on an image dataset. We conclude that the SVM is more attractive for the image classification task because it requires a much shorter training time, despite the fact that the BPM achieves slightly higher classification accuracy.

Forestry management requires careful and intensive planning efforts to ensure optimal yield, ecological stability, and regulatory compliance. We describe a method of identifying wetlands and producing maps of their extent from commonly available, remotely-sensed imagery. This method provides a large labor savings over both field inspections and manual photo inspections. The enhanced accuracy translates into better timber harvest planning and better conservation of the wetlands.

In this paper, we describe a novel content-based retrieval application which permits astrophysicists to search large image sequence archives for solar phenomenon, such as solar flares, based on the spatio-temporal behavior of the solar phenomenon. Specifically, images are preprocessed to identify bright and dark spots based on their relative intensity with respect to their neighboring regions. Temporally persistent objects are then extracted from the collection of spots, and their spatio-temporal behavior represented as intensity and size time series. Users define a query in terms of a model of spatio-temporal behaviors through a Web-based interface. The stored intensity and size time series are searched, and series segments that match the specified specified spatio-temporal behavior are returned. The benchmark results based on 2500 satellite images show that the proposed methodology demonstrated better than 85% accuracy on a solar phenomenon previously identified by astrophysicists.

We describe methods for content-based searching of images using MPEG-7 descriptions. The search problems range from matching of images based on global features to matching based on multiple objects, multiple features, and structural or semantic constraints and relationships. We provide a taxonomy of the different searching and matching problems and present query methods for each type. Furthermore, we examine methods for computing approximate answers for some of the searching problems in order to allow a trade-off of query response time and precision.

In this paper, we propose an active and inductive combined learning method to learn users’ image query concepts. We model query concepts in -CNF, which can be used to express most practical queries. To learn a user’s query concept, we propose MEGA. MEGA initializes a user’s query concept as the conjunction of all disjunctions of at most length of the predicates. It then intelligently selects unlabeled data to present to the user for gathering information to eliminate the maximum expected number of disjunctions. MEGA maximizes the usefulness of each example it generates for learning a user’s query concept and hence expedites the convergence to the target concept. Through analysis and experiments, we show that MEGA can learn a complex image query concept much faster than some traditional schemes.

As the earth science data and information products begin to proliferate due to the increased number of earth observing instruments and platforms, it has become increasingly difficult for the end consumer to leverage the wide variety of available earth science data and information products. In this paper, we propose an innovative infrastructure to enable the consumers to locate and tradeoff possible alternative earth science data and information sources in an electronic marketplace setting. Specifically, this architecture provides mechanisms to annotate the requests and offerings of the data and information products, to decompose the concepts of the requests and offerings to facilitate the matchmaking and inferencing. Based on the knowledge models developed for each application domain and science discipline, the matchmaking mechanism will be able to fuse and combine multiple alternative data and information sources so that the quality of the results can be maximized while the cost for data acquisition is minimizing.

As Internet information portals become prevalent for both Internet and Intranet, most existing Internet Application Server architectures are not scalable to support the large amount of personalization, customization and content adaptation required. We propose a framework to capture the information and content dissemination process. Furthermore, we propose a methodology to map this process to a distributed application server environment. By fully exploiting the intersections of user preference at multiple content processing stages, this new framework enables high hit ratio on processing, storage, and transmission of content and thus scales well to support a large number of clients.

This paper describes the Onion technique, a special indexing structure for linear optimization queries. Linear optimization queries ask for top-N records subject to the maximization or minimization of linearly weighted sum of record attribute values. Such query appears in many applications employing linear models and is an effective way to summarize representative cases, such as the top-50 ranked colleges. The Onion indexing is based on a geometric property of convex hull, which guarantees that the optimal value can always be found at one or more of its vertices. The Onion indexing makes use of this property to construct convex hulls in layers with outer layers enclosing inner layers geometrically. A data record is indexed by its layer number or equivalently its depth in the layered convex hull. Queries with linear weightings issued at run time are evaluated from the outmost layer inwards. We show experimentally that the Onion indexing achieves orders of magnitude speedup against sequential linear scan when N is small compared to the cardinality of the set. The Onion technique also enables progressive retrieval, which processes and returns ranked results in a progressive manner. Furthermore, the proposed indexing can be extended into a hierarchical organization of data to accommodate both global and local queries.

In this demo, we will show the implementation of a content-based SPatial Image Retrieval Engine (SPIRE) for multimodal unstructured data. This architecture provides a framework for retrieving multi-modal data including image, image sequence, time series and parametric data from large archives. Dramatic speedup (from a factor of 4 to 35) has been achieved for many search operations such as template matching, texture feature extraction. This framework has been applied and validated in solar flares and petroleum exploration in which spatial and spatial-temporal phenomena are located.

In this paper, we describe description schemes (DSs) for image, video, multimedia, home media, and archive content proposed to the MPEG-7 standard. MPEG-7 aims to create a multimedia content description standard in order to facilitate various multimedia searching and filtering applications. During the design process, special care was taken to provide simple but powerful structures that represent generic multimedia data. We use the extensible markup language (XML) to illustrate and exemplify the proposed DSs because of its interoperability and flexibility advantages. The main components of the image, video, and multimedia description schemes are object, feature classification, object hierarchy, entity-relation graph, code downloading, multi-abstraction levels, and modality transcoding. The home media description instantiates the former DSs proposing the 6-W semantic features for objects, and 1-P physical and 6-W semantic object hierarchies. The archive description scheme aims to describe collections of multimedia documents, whereas the former DSs only aim at individual multimedia documents. In the archive description scheme, the content of an archive is represented using multiple hierarchies of clusters, which may be related by entity-relation graphs. The hierarchy is a specific case of entity-relation graph using a containment relation. We explicitly include the hierarchy structure in our DSs because it is a natural way of defining composite objects, a more efficient structure for retrieval, and the representation structure used in MPEG-4. We demonstrate the feasibility and the efficiency of our description schemes by presenting applications that already use the proposed structures or will greatly benefit from their use. These applications are the visual apprentice, the AMOS-search system, a multimedia broadcast news browser, a storytelling system, and an image meta-search engine, MetaSEEk.

We present an adaptive wavelet view element framework for managing different types of multi-dimensional data in storage and retrieval applications. We consider the problems of multi-dimensional data compression, multi-resolution subregion access, selective materialization, progressive retrieval and similarity searching. The framework uses wavelets to partition the multi-dimensional data into view elements that form the building blocks for synthesizing views of the data. The view elements are organized and managed using different view element graphs. The graphs are used to guide cost-based view element selection algorithms for optimizing compression, access, retrieval and search performance.

Several years have passed since the research topic of content based multimedia retrieval emerged.We have witnessed the burgeoning research activities into a plenitude of new indexing, retrieval, and filtering tools for images, video, audio, music, graphics, and their combinations with text-based information.Exciting research opportunities arise when integrating knowledge from multiple disciplines, such as media content processing, database, information retrieval, and machine user interface.In the commercial domain, we have also witnessed several impressive efforts moving technologies into practical arenas.This panel includes experts from industry, research labs, and academia.The panel will assess the state of the art and articulate the important future directions in the general field of multimedia access and retrieval.

Growing numbers of pervasive devices are gaining access to the Internet and other information sources. However, much of the rich multimedia content cannot be easily handled by the client devices with limited communication, processing, storage and display capabilities. In order to improve access, we are developing a system for scalable delivery of multimedia. The system uses an InfoPyramid for managing and manipulating multimedia content composed of video, images, audio and text. The InfoPyramid manages the different variations of media objects with different fidelities and modalities and generates and selects among the alternatives in order to adapt the delivery to different client devices. We describe a system for scalable multimedia delivery for a variety of client devices, including PDAs, HHCs, smart phones, TV browsers and color PCs.

Content delivery over the Internet needs to address both the multimedia nature of the content and the capabilities of the diverse client platforms the content is being delivered to. We present a system that adapts multimedia Web documents to optimally match the capabilities of the client device requesting it. This system has two key components. 1) A representation scheme called the InfoPyramid that provides a multimodal, multiresolution representation hierarchy for multimedia. 2) A customizer that selects the best content representation to meet the client capabilities while delivering the most value. We model the selection process as a resource allocation problem in a generalized rate distortion framework. In this framework, we address the issue of both multiple media types in a Web document and multiple resource types at the client. We extend this framework to allow prioritization on the content items in a Web document. We illustrate our content adaptation technique with a web server that adapts multimedia news stories to clients as diverse as workstations, PDA's and cellular phones.

Timing skew has been the major limitation for high-speed synchronous operation of a VLSI system. In this paper, a statistical timing model that accounts for both static and random timing skew is proposed. Based on this model, we analyze the timing rules of a synchronous VLSI system consisting of multiple pipelined stages, establish the yield of the system as a function of its device characteristics, and derive the relationship between the maximum throughput of such a system and its timing skew. The following timing schemes are evaluated: conventional pipelining, in which the transmitter cannot initiate the next cycle until the receiver has received the data and wave pipelining, in which the transmitter initiates the next cycle as soon as the current data has been sent out. The results show that the yield of a VLSI system using either of the pipelining schemes exhibits threshold behavior for Gaussian distributed static skew. Furthermore, the system throughput is shown to be very sensitive to the random skew.

Article MALM: a framework for mining sequence database at multiple abstraction levels Share on Authors: Chung-Sheng Li IBM Thomas J.Watson Research Center, P.O. Box 704, Yorktown Heights, NY IBM Thomas J.Watson Research Center, P.O. Box 704, Yorktown Heights, NYView Profile , Philip S. Yu IBM Thomas J.Watson Research Center, P.O. Box 704, Yorktown Heights, NY IBM Thomas J.Watson Research Center, P.O. Box 704, Yorktown Heights, NYView Profile , Vittorio Castelli IBM Thomas J.Watson Research Center, P.O. Box 704, Yorktown Heights, NY IBM Thomas J.Watson Research Center, P.O. Box 704, Yorktown Heights, NYView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 267–272https://doi.org/10.1145/288627.288666Online:01 November 1998Publication History 38citation409DownloadsMetricsTotal Citations38Total Downloads409Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

High-dimensionality indexing of feature spaces is critical for many data-intensive applications such as content-based retrieval of images or video from multimedia databases and similarity retrieval of patterns in data mining.Unfortunately, the performance of nearest neighbor (NN) queries, which are required for similarity search, deteriorates rapidly with the increase in the number of dimensions.We propose the Clustering with Singular Value Decomposition (CSVD) method, which combines clustering and singular value decomposition (SVD) to reduce the numb e r o f i n d e x d i m e nsions, while maintaining a reasonably high precision for a given value of recall.In the proposed CSVD method, homogeneous points are grouped into clusters such that the points in each cluster are more amenable to dimensionality reduction than the original dataset.Experiments with texture vectors extracted from satellite images show that CSVD achieves signi cantly higher dimensionality reduction than SVD for the same fraction of total variance preserved.Conversely, for the same compression ratio CSVD results in an increase in preserved total variance with respect to SVD (e.g., a 70% increase for a 20:1 compression ratio).This translates to a higher eciency in processing approximate NN queries, as quanti ed through experimental results.

There is a growing need for developing a content description language for multimedia that improves searching, indexing and managing of the multimedia content. The MPEG group established the MPEG-7 effort to standardize the multimedia content interface. The proposed interface will bridge the gap between various types of content meta-data, such as content features, annotations, relationships, and the search engines. We develop a method of handling multimedia content description in a new multi-abstraction, multi-modal content representation framework called the InfoPyramid. The InfoPyramid facilitates the search, retrieval, manipulation, and transmission of multimedia data by providing a hierarchy for content descriptors. We illustrate the suitability of the InfoPyramid multimedia content description to MPEG-7 by examining four multimedia retrieval applications: a Web-image search engine, a satellite image retrieval system, an Internet content delivery system, and a TV news storage and retrieval system.

In this paper, we present a method for dynamically assembling views in multi-dimensional data cubes in order to more e#ciently support data analysis and querying involving aggregations. The proposed method decomposes the data cubes into an indexed hierarchy of view elements. The view elements di#er from traditional data cube cells in that they correspond to partial and residual aggregations of the data cube. The view elements provide highly granular building blocks for synthesizing the aggregated and rangeaggregated views of the data cubes. We propose a strategy for selecting and materializing the view elements based on the frequency of view access. This allows the dynamic adaptation of the view element sets to patterns of retrieval. We present a fast and optimal algorithm for selecting non-expansive view element sets that minimize the processing costs for generating a population of aggregated views. We also present a greedy algorithm for selecting redundant view element sets in order...

We have proposed and evaluated a scalable architecture for implementing multi-gigabit protocol engines. The architecture utilizes a combination of custom-made VLSI circuitry and a general-purpose processor, such as the Intel 960 or the IBM PowerPC 403. Time critical operations such as line coding/decoding, CRC generation/checking, context-independent header processing, and buffer management are implemented in the customized VLSI part. These designs are never-the-less scalable and can be cascaded to further increase throughput. Some of the packet level processing, such as context-dependent header processing, are performed by the general-purpose processor. As processing power increases, more and more functions can be included in the general purpose processor. The throughput of this architecture is shown to be adequate for the operations and bit rates currently specified by Fibre Channel. Future CMOS technology advances will have the potential to further improve the raw throughput.

In this paper, the performance of similarity retrieval from satellite image databases by using different sets of spatial and transformed-based texture features is evaluated and compared. A benchmark consisting of 37 satellite image clips from various satellite instruments is devised for the experiments. We show that although the proposed feature set perform only slightly better with the Brodatz set, its performance is far superior for the satellite images. The result indicates that more than 25% of the benchmark patterns can be retrieved with more than 80% accuracy by using normalized Euclidean distance. In contrast, less than 10% of the patterns are retrieved with more than 80% accuracy by using transformed-based feature sets (such as those based on Gabor filter or quadrature mirror filter (QMF)).

Block-based feature extraction and clustering algorithms usually have to trade off between resolution and accuracy, as larger image block tends to generate more representative features at the expense of clustering resolution. We propose a new postprocessing technique for optimally combining the labeling results from overlapping image regions. This technique, the modified maximum a posteriori (MMAP) method, utilizes both local and global information from the neighborhood of the image region under consideration. Consequently, the resolution of the clustering becomes independent of the accuracy of the feature. Experimental results show dramatic improvement of the classification accuracy over methods that do not postprocess the clustering labels with the MMAP algorithm.

We introduce a new framework for classifying large images (in the EOS; Earth Observing System) that is more accurate and less computationally expensive than the classical pixel-by-pixel approach. This approach, called progressive classification, is well suited for analyzing large images, such as multispectral satellite scenes, compressed with wavelet-based or block-transform-based transformations. These transformations produce a multiresolution pyramid representation of the data. A progressive classifier analyses the image at the coarsest resolution level, and it decides whether each coefficient corresponds to a homogeneous block of pixels in the original image or to a heterogeneous block. In the first case it labels the block, in the second case it recursively analyzes the region of the image at the immediately finer resolution level. Computational efficiency, compared to the classical approach, results from examining a much smaller number of coefficients than the number of pixels in the original image. Thus, progressive classification is a prime candidate as a content-based search operator for remotely-sensed data.

We present a hierarchical algorithm, HierarchyScan, that efficiently locates one-dimensional subsequences within a collection of sequences of arbitrary length. The subsequences identified by HierarchyScan match a given template pattern in a scale- and phase-independent fashion. The idea is to perform correlation between the stored sequences and the template in the transformed domain hierarchically. Only those subsequences whose maximum correlation value is higher than a predefined threshold will be selected. The performance of this approach is compared to the sequential scanning and an order-of-magnitude speedup is observed.

We consider real-time traffic in a heterogeneous internetworking environment with IP routers, MAC bridges, hubs, switched LANs etc. We assume that the current routing protocols remain unchanged. However in this environment, in order to provide quality of service (QoS): bandwidth, delay, constant-bounded jitter and no-loss due to congestion, we suggest a new flow control function called time-driven priority, which is an internal traffic shaping mechanism. We show how it supports two classes of connections: constant bit rate (CBR) with deterministic guarantees, and variable bit rate (VBR) with statistical multiplexing. The mechanism does not require to identify and separate the packet flows of different real-time sessions/connections inside the network. As a result, it achieves lower switching complexity when compared with other internal traffic shaping methods. As consequences of the time-driven priority mechanism we further achieve: (1) QoS parameters which are independent of the connection bandwidth, (2) QoS parameters which are independent of the existing heterogeneous internetworking asynchronous data traffic and (3) the capability for policing and securing the network QoS.

The paper discusses a control scheme for broadband switching systems consisting of a hybrid multilevel control network and a three-phase commit control protocol. The proposed architecture and protocol allow for fast connection setup in point-to-point connections while efficiently handling multicast and broadcast connections. The consistency of system status is maintained through the three-phase commit procedure. The optimal control configuration to minimize the connection setup service time is derived by using both analytical and simulation approaches.

This paper presents a new distributed methodology for source destination synchronization for interactive teleconferencing. The method is based on a reference clock, which is synthesized from a distributed global clock. The global clock is generated by periodically exchanging inband synchronization signals with neighboring nodes. The timing jitter achieved with this method can be arbitrarily close to the jitter obtained by the centralized synchronous methods which usually use an out-of-band, hard-wired reference clock. The global clock synchronization algorithm, used in this work, guarantees frequency locking of all the network nodes to the slowest clock in the system. As a result, the slowest clock can be used as an implicit reference clock for source-destination synchronization protocols, such as synchronous frequency encoding technique (SFET) and synchronous residual time stamp (SRTS). This inband synchronization method does not require the explicit knowledge of which clock is actually the slowest in the system. Therefore, if the slowest clock fails, then another clock on a different node will be the slowest, and the nodes will use it as a reference clock for the source-destination synchronization protocol. The existing out-of-band reference clock techniques do not have this strong fault tolerant property.

We describe a method of controlling a three-stage Clos nonblocking switch where "speculative" self-routing over the Clos fabric is augmented with reservations over a control network that connects controllers in the input and output stages of the switch. The effect is that most connections succeed over the speculative path while those subject to contention are processed over the control network. We present simulation result which indicate that the inclusion of a control network yields significant benefits under heavily nonuniform traffic conditions.

No abstract available.

This paper shows how to design an ATM network, for real-time traffic, such that under full network load (i) the maximum delay of a low-rate voice connection is minimized, (ii) the delay uncertainty or jitter is a fixed network parameter, and (iii) the required buffer sizes (inside the network) to ensure loss-free routing is minimized. In addition, this design does not exclude the integration of other classes of traffic, such as connectionless, which have lower priority. The isochronous timing information is used only for regulating and pacing the traffic forwarded inside the network rather than for routing as in traditional circuit switching networks. This means that an ATM cell is sent from one switch to another not at a very specific time but within a time frame of a relatively long duration as compared to the cell transmission time. The routing of cells of each connection is based on VCI and VPI, and as a result, timing errors do not affect the ATM routing. Analytical and simulation results are presented, which demonstrate the effectiveness of this switching approach.>

Optical metropolitan/wide area networks with arbitrary topology usually require optical amplifiers to compensate for the fiber attenuation and splitting loss introduced by the star couplers. An amplifier placement algorithm using spanning tree traversal is proposed. Using this algorithm, the authors show that it is possible to equalize the arriving optical signals at each receiver of the network. This technique enhances other static or dynamic gain equalization schemes and presents a more effective and robust solution to the power equalization problem for WDMA networks with arbitrary topology.>

A buffer insertion ring with network access fairness control for gigabit LANs is proposed. The basic buffer insertion ring has the maximum spatial reuse on the multiple ring segments by allowing concurrent traffic, but it suffers from the possibility of station starvation. To solve this problem, a notion of adjustable fairness based on mutual interference between pairs of stations in bandwidth usage is defined. The adjustable fairness definition serves as a basis for conditions to trigger backpressure signals. A cooperative backpressure mechanism is invented for flow regulation. The effectiveness of the proposed network access mechanism is demonstrated by comparisons with other buffer insertion rings based on simulations.>

This work presents the design details and experimental results for a parallel optical link. The link is designed for connections within high-speed digital systems, specifically for board- and backplane-level interconnections. The link can contain as many fibers in parallel as technology permits. The unusual aspects of this interconnection system are that it is DC-coupled and uses fully differential inputs, two optical channels per signal, to achieve self-thresholding and noise immunity. A chip set consisting of a 2.5-Gb/s bipolar differential laser driver, a 800-Mb/s GaAs MSM (metal-semiconductor-metal) preamplifier array, a 800-Mb/s GaAs MSM preamplifier-postamplifier array, and a GaAs MSM preamplifier array in which each preamplifier has a different bandwidth varying from 300 Mb/s to 2 Gb/s has been designed, fabricated, and tested to serve as a vehicle for verifying the concept. Although the experimental testing of the entire interconnect system is not yet complete, the experimental studies presented show a bandwidth in excess of 800 MHz and excellent signal isolation between channels.>