Nowadays, social network sites; such as Facebook and Twitter, have tremendous number of users in their repositories. Having this huge amount of data requires analyzing them to get statistics about the users and their interests. In this paper, we propose a new algorithm that clusters the nodes in social networks into communities based on their geodesic location and the similarity between their interests. The algorithm is examined thoroughly to test its performance. The experiments show that the algorithm achieves a high community detection accuracy.

A phenomenon appears in a sensor network when a group of sensors persist to generate similar behavior over a period of time. PhenomenaBases (or databases of phenomena) are equipped with phenomena detection and tracking (PDT) techniques that continuously run in the background of a sensor database system to detect new phenomena and to track already existing phenomena. The process of phenomena detection and tracking is initiated by a multi-way join operator that comes at the core of PDT techniques to report similar sensor readings. With the increase in the sensor network size, the join operator (and, consequently, query processing in the PhenomenaBase) face several scalability challenges. In this paper, we present a join operator for PhenomenaBases (the SNJoin operator) that is specially-designed for dynamically-configured large-scale sensor networks with distributed processing capabilities. Experimental studies illustrate the scalability of the proposed join operator in PhenomenaBases with respect to the number of detected phenomena and the output delay

A new multimedia disk-scheduling algorithm, termed Cascaded-SFC, is presented. The Cascaded-SFC multimedia disk scheduler is applicable in environments where multimedia data requests arrive with different quality of service (QoS) requirements such as real-time deadline and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. The Cascaded-SFC disk scheduler provides a unified framework for multimedia disk scheduling that scales with the number of scheduling parameters. The general idea is based on modeling the multimedia disk requests as points in multiple multidimensional subspaces, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number, and a third dimension represents the priority of the request, etc.). Each multidimensional subspace represents a subset of the QoS parameters that share some common scheduling characteristics. Then the multimedia disk scheduling problem reduces to the problem of finding a linear order to traverse the multidimensional points in each subspace. Multiple space-filling curves are selected to fit the scheduling needs of the QoS parameters in each subspace. The orders in each subspace are integrated in a cascaded way to provide a total order for the whole space. Comprehensive experiments demonstrate the efficiency and scalability of the Cascaded-SFC disk scheduling algorithm over other disk schedulers.

A new quality of service (QoS) aware disk scheduling algorithm is presented. It is applicable in environments where data requests arrive with different QoS requirements such as real-time deadline, and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. A unified framework for QoS disk scheduling is presented that scales with the number of scheduling parameters. The general idea is based on modeling the disk scheduler requests as points in the multi-dimensional space, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number and a third dimension represents the priority of the request, etc.). Then the disk scheduling problem reduces to the problem of finding a linear order to traverse these multi-dimensional points. Space-filling curves are adopted to define a linear order for sorting and scheduling objects that lie in the multi-dimensional space. This generalizes the one-dimensional disk scheduling algorithms (e.g., EDF SATF, FIFO). Several techniques are presented to show how a QoS-aware disk scheduler deals with the progressive arrival of requests over time. Simulation experiments are presented to show a comparison of the alternative techniques and to demonstrate the scalability of the proposed QoS-aware disk scheduling algorithm over other traditional approaches.

Modern video servers support both video-on-demand and nonlinear editing applications. Video-on-demand servers enable the user to view video clips or movies from a video database, while nonlinear editing systems enable the user to manipulate the content of the video database. Applications such as video and news editing systems require that the underlying storage server be able to concurrently record live broadcast information, modify prerecorded data, and broadcast an authored presentation. A multimedia storage server that efficiently supports such a diverse group of activities constitutes the focus of this study. A novel real-time disk scheduling algorithm is presented that treats both read and write requests in a homogeneous manner in order to ensure that their deadlines are met. Due to real-time demands of movie viewing, read requests have to be fulfilled within certain deadlines; otherwise, they are considered lost. Since the data to be written into disk is stored in main memory buffers, write requests can be postponed until critical read requests are processed. However, write requests still have to be processed within reasonable delays and without the possibility of indefinite postponement. This is due to the physical constraint of the limited size of the main memory write buffers. The new algorithm schedules both read and write requests appropriately, to minimize the amount of disk reads that do not meet their presentation deadlines, and to avoid indefinite postponement and large buffer sizes in the case of disk writes. Simulation results demonstrate that the proposed algorithm offers low violations of read deadlines, reduces waiting time for lower priority disk requests, and improves the throughput of the storage server by enhancing the utilization of available disk bandwidth.

We introduce a new deadline driven disk scheduling algorithm designed for multimedia servers. The proposed algorithm supports real time requests with multiple priorities, e.g., those for different object classes in digital library applications. The proposed algorithm enhances utilization of disk bandwidth by: maintaining one queue for all requests; and optimizing the seek time. Prior schemes, collectively termed "multi-queue schemes", maintain a separate queue for each priority group and optimize the performance of the high priority requests only. When compared with our proposed scheme, our technique provides approximately two order of magnitude improvement in meeting the deadline of low priority requests. In addition, this algorithm provides both a better disk utilization and a better average response time. Under certain conditions, our algorithm violates the deadline of a few high priority requests (less than 5 out of a million requests).

This paper presents an indexing method that can be used to search a large collection of cursive handwriting. The basic idea is to segment each cursive string into a set of strokes. Each of these strokes can be described with a set of features and, thus, can be stored as points in the feature space. The Karhuraen-Lor%e transform is then used to minimize the number of features used (data dimensionality) and thus the index size. Feature vectors are stored in an R-tree. Similarity search can be performed by executing a few range queries and then applying a simple voting algorithm to the output to select the strings that are most similar to the query. The proposed index can support similarity queries as well as substring mat thing. It is resilient to the kind of errors that result from the segmentation process, namely, stroke insertion/deletion and m-n substitution.

Article Free Access Share on The time index+: an incremental access structure for temporal databases Authors: Vram Kouramajian Department of Computer Science, The Wichita State University, Wichita, Kansas Department of Computer Science, The Wichita State University, Wichita, KansasView Profile , Ibrahim Kamel Department of Computer Science, University of Maryland, College Park, Maryland Department of Computer Science, University of Maryland, College Park, MarylandView Profile , Ramez Elmasri Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile , Syed Waheed Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile Authors Info & Claims CIKM '94: Proceedings of the third international conference on Information and knowledge managementNovember 1994 Pages 296–303https://doi.org/10.1145/191246.191298Online:29 November 1994Publication History 17citation423DownloadsMetricsTotal Citations17Total Downloads423Last 12 Months6Last 6 weeks1 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

We propose the concept of fractal dimension of a set of points, in order to quantify the deviation from the uniformity distribution. Using measurements on real data sets (road intersections of U.S. counties, star coordinates from NASA's Infrared-Ultraviolet Explorer etc.) we provide evidence that real data indeed are skewed, and, moreover, we show that they behave as mathematical fractals, with a measurable, non-integer fractal dimension.

We propose new R-tree packing techniques for static databases.Given a collection of rectangles, we sort them and build the R-tree bottom-up.There are several ways time of a range query as a function of the geometric characteristics of the R-tree.

We consider the problem of exploiting parallelism to accelerate the performance of spacial access methods and specifically, R-trees [11]. Our goal is to design a server for spatial data, so that to maximize the throughput of range queries. This can be achieved by (a) maximizing parallelism for large range queries, and (b) by engaging as few disks as possible on point queries [22].