Universität Rostock, Lehrstuhl Datenbank- und Informationssysteme: Dbis Repository
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A Framework for Self-managing Database Support and Parallel Computing for Assistive Systems
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Generating Privacy Constraints for Assistive Environments
Get PDFSmart environments produce large amounts of data by a plurality of sensors, which constantly track our activities and desires. To support our daily life, assistive environments process these data to calculate our intentions and future actions. In many cases, more information than required are generated and processed by the assistive
system. Thereby, the system can learn more about the user than intended. By this, the users’ right to informational self-determination is injured, because they lose control
how their data is used.
In this paper, we present a model to let the user formulate requirements to protect his privacy in smart environments. These requirements are transformed into multiple
integrity constraints, which ensure privacy
Typisierte Algebra-Operationen in einem Hypergraph-Datenbanksystem für Anwendungen in der Volkskunde
No full textOptimization of position finding step of PCM-oMaRS algorithm with statistical information
No full textThe 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
Optimierung objektorientierter Anfragen: Das Projekt CROQUE
No full textDieser Artikel beschreibt einige Aspekte der Optimierer-Komponente des CROQUE-Systems. Das im CROQUE-Projekt entwickelte objektorientierte Anfrageverarbeitungs-Framework bietet eine deskriptive Anfrageschnittstelle auf Basis von ODMG OQL, physische Datenunabhängigkeit, wie sie in heute kommerziell verfügbaren Produkten noch nicht umgesetzt wurde und den hier beschriebenen Anfrage-Optimierer, dessen Integration in das Gesamtprojekt vorgesehen ist. Insgesamt bietet der Anfrage-Optimierer einige interessante neuartige Features, die die Effizienz des Anfrage-Rewriting und der Anfrage-Auswertung positiv beeinflussen sollen
Reducing Sensors according to a Vectors Analysis of stored measurements (ReSeVA)
No full textThe 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
PCM- oMaRS Algorithm: Parallel Computation of Median - omniscient Maximal Reduction Steps
Get PDFThe 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