PCM- oMaRS Algorithm: Parallel Computation of Median - omniscient Maximal Reduction Steps

The goal of a distributed computation algorithm is to determine the result of a function of numerical elements, which are distributed in n multi sets.It is known that computation of holistic aggregation functions on distributed multi sets indeed requires more work than non holistic aggregation functions. But with this article we will prove that the computation of a holistic function, which named exact median, can be computed efficiently by providing both a candidate finding and a deterministic location algorithms which computes the position of exact median, dispelling the misconception that solving distributed median computation through parallel aggregation is infeasible. Some of most important part in Big Data field is to evaluate massive data values. A special case in this field is the calculation of kthsmallest values (specially the median) of distributed multi sets containing enormous data. Many approximation algorithms and algorithms with iterative or recursive steps of determination of median give solutions for the computation of median. But firstly sometime approximate value is dangerous for some data evaluation projects or researchs and secondly with other algorithms, the data blocking time is too long through the iteration or the recursion between global node and local nodes. This article focuses on a solution that gives a best effectively computation for this problem named PCM-oMaRS algorithm. The PCM-oMaRS algorithm guarantees the maximal reduction steps of the computation of the exact median in distributed multi sets and proves that we can compute the exact median effectively without needing the usage of recursive or iterative methods at the global communication level, which reduces the blocking time maximally. This algorithm provides more efficient execution not only in distributed multi sets even in local multi set with enormous data

A Decision Support System For Construction Equipment Management Based On Data Warehousing Technique

  As construction equipment is as considered the most important physical assets in construction companies. The effective equipment management, which is required to take the necessary operational and strategic decisions, is an urgent need to achieve the success of those companies. This include purchase, operation, retirement , renting, maintenance and repair..etc, with the least cost of operation as well as the best investment of the capital. The evolution in the use of automation technology and information management system associated with the increasing volume of collected equipment data, shows the need to a tool which enables to take an advantage of this huge collected data in equipment management.   This research suggests a data warehouse for construction equipment management of engineering companies in the Syrian Public Sectors. This will improve the sources of data for the knowledge discovery. In addition, this research presents a primary decision support system which allows a visual analysis of the equipment data at different levels of details that helps equipment managers to fix the hidden problems behind operating equipment and then provide decision-making of equipment management at high level of flexibility

Optimization of XQuery at XML Documents in digital Libraries

Create integrated schema by the use of XQuery respectively XQuery Core. Dissolve frequent conflicts of the integration by means of XQuery. Considering of Global and local aspects of optimizing XQuer

XQuery-Optimierung und -Zerlegungsstrategie im Zusammenhang mit XML-Schemaintegration

In der Vergangenheit wurde ein Vielzahl von digitalen Bibliotheken und elektronischen Katalogen durch Verlage, Bibliotheken und andere Einrichtungen bereitgestellt, um Publikationen bzw. die zugehörigen bibliographischen Metadaten zu Publikationen (zum Beispiel Autoren, Titel, Verlag) einer großen Anzahl von Benutzern Über entsprechende Dienste zur Recherche zur Verfügung zu stellen. Die Sprachen XQuery ist gut geeignet für Schematransformationen, Sicht-Definitionen und Konfliktbehandlung. Um die Recherche nach bibliographischen XML-Daten von Publikationen und die Suche im Dokument selbst zu erleichtern, ist eine Integration von vorhandenen digitalen Bibliotheken mit dem Ziel notwendig, einheitliche Zugriffschnittstellen auf die gespeicherten Informationen bereitzustellen. Nach der Erstellung des globalen XML-Schemas durch Verwendung der Anfragesprache XQuery wird XQuery auch für globale Anfragen an das erstellte Schema verwendet. Die andere Richtung der Bearbeitung einer Anfrage er-möglicht es uns die globale Anfrage in lokale Teilanfragen zu zerlegen. Die Optimierung komplexer XQuery-Ausdrücke ist für die effiziente Auswertung von Anfragen notwendig. Die algebraische Optimierung ist der Hauptbaustein der Optimierung und basiert auf einer Algebra. Da es keine Standardalgebra für die Anfragesprache XQuery gibt, ist es notwendig eine Algebra zu entwickeln. Die Optimierung der Anfragesprache XQuery ist also für die Integration, die Zerlegung und die Verarbeitung der Anfrage in einem Datenbanksystem sehr wichtig

Optimization of position finding step of PCM-oMaRS algorithm with statistical information

The PCM- oMaRS algorithm guarantees the maximal reduction steps of the computation of the exact median in distributed datasets and proved that we can compute the exact median effectively with reduction of blocking time and without needing the usage of recursive or iterative methods anymore. This algorithm provided more efficient execution not only in distributed datasets even in local datasets with enormous data. We cannot reduce the steps of PCM- oMaRS algorithm any more but we have found an idea to optimize one step of it. The most important step of this algorithm is the step in which the position of exact median will be determinate. For this step we have development a strategy to achieve more efficiency in determination of position of exact median. Our aim in this paper to maximize the best cases of our algorithm and this was achieved through dividing the calculation of number of all value that smaller than or equal to temporary median in tow groups. The first one contains only the values that smaller than the temporary median and the second group contains the values that equal to the temporary median. In this dividing we achieve other best cases of PCM- oMaRS algorithm and reducing the number of values that are required to compute the exact median. The complexity cost of this algorithm will be discussed more in this article. In addition some statistical information depending on our implementation tests of this algorithm will be given in this paper

Reducing Sensors according to a Vectors Analysis of stored measurements (ReSeVA)

The recognition of motion is widely used in games development field but it is active too in care systems. Recognition of motion according measurements needs data (values) from many sensors, like Position, Velocity, Acceleration, Orintation, etc. We have two major ways to determine which placements of sensors on the body are required to recognize the motion. The first way connects its work with the results of other science branches like sports science and game development. The other one depends on the following strategy. Many sensors were placed on the body, without the knowledge, which sensors are required. Then according an analysis of the stored data for each sensors, the behavioral similarity of these sensors will be extracted. The target of both ways is to reduce the cost of building a suit of sensors, and simultaneously to keep the results of the recognition of motion correct. In this paper we follow the second way and define a new regression analysis “ReSeVA” depending on vector definition (on its angles and longs) and on the principle of Newton’s law of metion