Improved Algorithm for Distributed Points Positioning Using Uncertain Objects Clustering

Positioning of mobile objects that require communication with some kind of online service application is a very challenging task. Proper positioning with minimal deviation is an important mobile service system (MSS), e.g. taxi service used in this paper. It will perform all tasks for the users and reduce the overall travel distance. This paper is focused on the development of an algorithm that will find the optimal position for an MSS object and upgrade the system quality using uncertain data clustering. If the best position for the MSS is found, then the response time is short, and the system tasks could also be performed in usable time. The improved bisector pruning method is used for clustering stored data of mobile service system objects to provide the best position of system objects. As the best position of MSS objects, we use cluster centres. Using clustering, the total expected distance from end users to the service system is minimal. Therefore, the MSS is more efficient and has more time to fulfil additional tasks at the same time

Improved bisector clustering of uncertain data using SDSA method on parallel processors

Razvrstavanje podataka s nesigurnošću je vrlo istraživano područje. Ovaj rad posvećen je razvrstavanju objekata koji imaju nesigurnost 2D položaja uzrokovanog gibanjem objekata. Položaj pokretnog objekta izvještava se periodički, i stoga položaj objekta sadrži nesigurnost i opisan je funkcijom gustoće razdiobe (PDF). Podaci o takvim objektima i njihovim položajima čuvaju se u distribuiranim bazama podataka. Broj objekata s nesigurnošću može biti jako velik i dobivanje kvalitetnog rezultata u razumnom vremenu je zahtijevan zadatak. Najjednostavnija metoda za razvrstavanje je UK-means, u kojoj se računaju sve očekivane udaljenosti (ED) od objekata do središta grozdova. Stoga je UK-means nedjelotvorna metoda. Kako bi se izbjeglo računanje očekivanih udaljenosti predstavljene su brojne metode za odbacivanje. U radu je dan pregled postojećih metoda i predložena kombinacija dviju metoda. Prva metoda je nazvana podjela područja skupa podataka (SDSA) i kombinirana je s poboljšanom simetralnom metodom kako bi se skratilo vrijeme razvrstavanja podataka s nesigurnošću. Pomoću SDSA metode područje skupa podataka je podijeljeno na mala pravokutna područja i promatraju se samo objekti koji se nalaze u tom području. Koristeći mala pravokutna područja nudi se mogućnost za paralelno procesiranje, jer su područja međusobno neovisna i mogu se računati na različitim jezgrama procesora. Provedeni su pokusi kako bi se pokazala uspješnost nove kombinirane metode.Clustering uncertain objects is a well researched field. This paper is concerned with clustering uncertain objects with 2D location uncertainty due to object movements. Location of moving object is reported periodically, thus location is uncertain and described with probability density function (PDF). Data about moving objects and their locations are placed in distributed databases. Number of uncertain objects can be very large and obtaining quality result within reasonable time is a challenging task. Basic clustering method is UK-means, in which all expected distances (ED) from objects to clusters are calculated. Thus UK-means is inefficient. To avoid ED calculations various pruning methods are proposed. A survey of existing clustering methods is given in this paper and a combination of two methods is proposed. The first method, called Segmentation of Data Set Area is combined with Improved Bisector pruning to improve execution time of clustering uncertain data. In SDSA method, data set area is divided in many small segments, and only objects in that small segment are observed. Using segments there is a possibility for parallel computing, because segments are mutually independent, thus each segment can be computed on different core of parallel processor. Experiments were conducted to evaluate the effectiveness of the combined methods

Symmetrical classification of service system objects

U ovoj disertaciji predložena su dva nova postupka za razvrstavanje nesigurnih objekata. Prvi postupak omogućuje razvrstavanje prostornih podataka zasnovano na simetralnoj podjeli prostora, te usporedbi i odbacivanju grozdova. Postupak znatno smanjuje broj računanja očekivanih udaljenosti i ubrzava proces odbacivanja grozdova u odnosu na postojeće postupke. Drugi postupak dijeli područja skupa objekata određivanjem prostornih odnosa objekata s ciljem povećanja mogućnosti paralelne obrade. Postupkom je omogućeno paralelno izvođenje procesa razvrstavanja. Postupak ne zahtijeva dodatna ulaganja u opremu, jer se može izvoditi na računalu s više jezgri. Razvijeni postupci iskorišteni su za stvaranje modela predviđanja ponašanja uslužnog sustava razvrstavanjem postojećih podataka o objektima uslužnog sustava. Pomoću modela mogu se predvidjeti zahtjevi za uslužnim sustavom i djelovati prema zahtjevima. Postiže se smanjenje troškova uslužnog sustava i povećava broj zadataka koje uslužni sustav može obaviti.Two original procedures for clustering spatially uncertain data are proposed in this dissertation. The first procedure enables the clustering of spatial data based on bisector division of space, using comparison and cluster pruning. It significantly reduces the number of the expected distances calculations and speeds up the process of clusters pruning in comparison to existing procedures. Second procedure divides the data set area using spatial relations among objects to increase the possibility of parallel processing. The procedure enables parallel execution of the clustering process. The procedure does not require additional investments in equipment, because of use a computer with multiple cores. Presented procedures are used for creating a model for prediction of behaviour service-oriented system, using clustering of existing data about objects of service-oriented system. This model can predict the requirements for service-oriented system and prepare according to the requirements. Costs are reduced and increased the number of tasks that can be done by service-oriented system