Software process measuring model

U ovom radu opisan je Model mjerenja softverskog procesa (MMSP). MMSP je metoda procjene softverskih procesa, kvantitativnog mjerenja i unapređenja procesa za organizacije koje se bave razvojem softvera (SPO). Metoda je razvijena dijelom na temelju poboljšanja metoda CMM/CMMI, Bootstrap i SPICE, i na standardima ESA PSS05 i ISO 90003. U žarištu MMSP-a je proces razvoja softvera u softverskim poduzećima. Članak objašnjava glavni koncept dobavljanja podataka o softverskim inženjerskim organizacijama i njihovim projektima pomoću temeljito izgrađenog upitnika. MMSP se može interpretirati kao metoda za opisivanje kakav je položaj organizacije i koje se promjene predlažu u slijedećim koracima. Osnovna ideja MMSP-a je utvrditi profil zrelosti procesa SPO-a. Ciljevi MMSP procjene su: a) izmjeriti i razviti profil zrelosti kvalitete procesa prikazom jakih i slabih strana procijenjenog SPŠO-a, b) derivirati korake za unapređenja iz prikazanog profila kvalitete procesa. Prikazan je rezultat procjene obavljene u jedan dan u organizaciji koja se bavi proizvodnjom softvera (SPO X) i Projekta X unutar SPO-a X koji je održan početkom listopada 2010. Rezultati procjene prikazuju ukupne organizacijske i metodološke razine za Projekt X. Organizacija je na razini zrelosti od 2,83. Metodologija je na razini zrelosti od 2,48. Ukupna razina zrelosti za organizaciju SPO X je na razini zrelosti od 2,42, dok je metodologija na razini zrelosti od 2,57. Organizacija članka je sljedeća: nakon uvoda u poglavlju jedan, poglavlje dva objašnjava razloge razvoja sustava MMSP. Poglavlje tri opisuje razvoj MMSP-a. Algoritam razina zrelosti je prikazan u slijedećem poglavlju. Poglavlje pet objašnjava evaluaciju SPO-a, rezultati procjene prikazani su u poglavlju šest. Poglavlje sedam sadrži zaključak, popis literature je u poglavlju osam.In this paper the Software Process Measuring Model (SPMM) is described. SPMM is a method for software process assessment, quantitative measurement and improvement for software producing organizations (SPOs). It has been developed partly based on a renovation of the CMM/CMMI, Bootstrap and SPICE methods, standards ESA PSS 05, and ISO 90003. SPMM focuses on the software development process in software production enterprises. The article explains the central concept of gaining data about software engineering organizations with a thoroughly constructed questionnaire. It gives a ground to measure the quality maturity level of organization and its projects. The SPMM can be interpreted as a method for describing where an organization stands and what changes are to be recommended in the next steps. The main idea of the SPMM is to determine the process maturity profile of an SPO. The goals of a SPMM self-assessment are: a) to measure and develop an SPO maturity quality profile showing strengths and weaknesses of the SPO assessed, b) to derive the steps for improvement from the shown quality profile. The result of one day assessment in software production organization X (SPO X), and Project X within the SPO X which was held at the beginning of October 2010 is presented. The result of the assessment showed the total organization and methodology maturity levels of the Project X. The organization is on maturity level 2,83. The methodology is on maturity level of 2,48. The total maturity level of the organization of SPO X is on maturity level of 2,42, and the methodology is on maturity level of 2,57. The organization of the paper is as follows: after the introduction in section one, section two explains the reasons of the SPMM development. Section three depicts the SPMM development. The maturity level algorithm is explicated in the next section. Section five explains the evaluation of the SPO, the assessment results are in section six. The conclusion is given in section seven, and the list of literature in section eight

Appointment scheduling system in multi doctor/multi services environment

Appointment scheduling systems are used by health care providers to manage access to their services. In this paper an algorithm and a web application for automatic appointment scheduling is presented. Both are implemented using the concept of booking appointments for patients for a specific service offered by each doctor. The purpose of the application is to make signing up for a specific service easier for patients and to improve health tourism in Croatia by maximizing doctor’s efficiency and minimize patient waiting time. Medical providers are added to the system, they add the services which they provide, and each service offered has its own duration time. Users register, search for services matching their parameters, and schedule an appointment for the requested service. Available appointments are generated using the presented algorithm, which is the main part of this paper. The algorithm searches the database and returns possible appointments. If patient has more than one appointment, possible appointments time can be before the existing appointment, between two appointments, or at the end of the last appointment. Thus, web application enables the patient to reserve desirable appointment time

Creation of Warehouse Models for Different Layout Designs

Warehouse is one of the most important components in logistics of the supply chain network. Efficiency of warehouse operations is influenced by many different factors. One of the key factors is the racks layout configuration. A warehouse with good racks layout may significantly reduce the cost of warehouse servicing. The objective of this paper is to give a scheme for building warehouses models with one-block and two-block layout for future research in warehouse optimization. An algorithm for creating a model database of a warehouse is introduced

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

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

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

Protecting Information with Subcodstanography

In modern communication systems, one of the most challenging tasks involves the implementation of adequate methods for successful and secure transfer of confidential digital information over an unsecured communication channel. Many encryption algorithms have been developed for protection of confidential information. However, over time, flaws have been discovered even with the most sophisticated encryption algorithms. Each encryption algorithm can be decrypted within sufficient time and with sufficient resources. The possibility of decryption has increased with the development of computer technology since available computer speeds enable the decryption process based on the exhaustive data search. This has led to the development of steganography, a science which attempts to hide the very existence of confidential information. However, the stenography also has its disadvantages, listed in the paper. Hence, a new method which combines the favourable properties of cryptography based on substitution encryption and stenography is analysed in the paper. The ability of hiding the existence of confidential information comes from steganography and its encryption using a coding table makes its content undecipherable. This synergy greatly improves protection of confidential information

Distributed System Development with ScalaLoci

Distributed applications are traditionally developed as separate modules, often in different languages, which react to events, like user input, and in turn produce new events for the other modules. Separation into components requires time-consuming integration. Manual implementation of communication forces programmers to deal with low-level details. The combination of the two results in obscure distributed data flows scattered among multiple modules, hindering reasoning about the system as a whole. The ScalaLoci distributed programming language addresses these issues with a coherent model based on placement types that enables reasoning about distributed data flows, supporting multiple software architectures via dedicated language features and abstracting over low-level communication details and data conversions. As we show, ScalaLoci simplifies developing distributed systems, reduces error-prone communication code and favors early detection of bugs

Software process measuring model

U ovom radu opisan je Model mjerenja softverskog procesa (MMSP). MMSP je metoda procjene softverskih procesa, kvantitativnog mjerenja i unapređenja procesa za organizacije koje se bave razvojem softvera (SPO). Metoda je razvijena dijelom na temelju poboljšanja metoda CMM/CMMI, Bootstrap i SPICE, i na standardima ESA PSS05 i ISO 90003. U žarištu MMSP-a je proces razvoja softvera u softverskim poduzećima. Članak objašnjava glavni koncept dobavljanja podataka o softverskim inženjerskim organizacijama i njihovim projektima pomoću temeljito izgrađenog upitnika. MMSP se može interpretirati kao metoda za opisivanje kakav je položaj organizacije i koje se promjene predlažu u slijedećim koracima. Osnovna ideja MMSP-a je utvrditi profil zrelosti procesa SPO-a. Ciljevi MMSP procjene su: a) izmjeriti i razviti profil zrelosti kvalitete procesa prikazom jakih i slabih strana procijenjenog SPŠO-a, b) derivirati korake za unapređenja iz prikazanog profila kvalitete procesa. Prikazan je rezultat procjene obavljene u jedan dan u organizaciji koja se bavi proizvodnjom softvera (SPO X) i Projekta X unutar SPO-a X koji je održan početkom listopada 2010. Rezultati procjene prikazuju ukupne organizacijske i metodološke razine za Projekt X. Organizacija je na razini zrelosti od 2,83. Metodologija je na razini zrelosti od 2,48. Ukupna razina zrelosti za organizaciju SPO X je na razini zrelosti od 2,42, dok je metodologija na razini zrelosti od 2,57. Organizacija članka je sljedeća: nakon uvoda u poglavlju jedan, poglavlje dva objašnjava razloge razvoja sustava MMSP. Poglavlje tri opisuje razvoj MMSP-a. Algoritam razina zrelosti je prikazan u slijedećem poglavlju. Poglavlje pet objašnjava evaluaciju SPO-a, rezultati procjene prikazani su u poglavlju šest. Poglavlje sedam sadrži zaključak, popis literature je u poglavlju osam.In this paper the Software Process Measuring Model (SPMM) is described. SPMM is a method for software process assessment, quantitative measurement and improvement for software producing organizations (SPOs). It has been developed partly based on a renovation of the CMM/CMMI, Bootstrap and SPICE methods, standards ESA PSS 05, and ISO 90003. SPMM focuses on the software development process in software production enterprises. The article explains the central concept of gaining data about software engineering organizations with a thoroughly constructed questionnaire. It gives a ground to measure the quality maturity level of organization and its projects. The SPMM can be interpreted as a method for describing where an organization stands and what changes are to be recommended in the next steps. The main idea of the SPMM is to determine the process maturity profile of an SPO. The goals of a SPMM self-assessment are: a) to measure and develop an SPO maturity quality profile showing strengths and weaknesses of the SPO assessed, b) to derive the steps for improvement from the shown quality profile. The result of one day assessment in software production organization X (SPO X), and Project X within the SPO X which was held at the beginning of October 2010 is presented. The result of the assessment showed the total organization and methodology maturity levels of the Project X. The organization is on maturity level 2,83. The methodology is on maturity level of 2,48. The total maturity level of the organization of SPO X is on maturity level of 2,42, and the methodology is on maturity level of 2,57. The organization of the paper is as follows: after the introduction in section one, section two explains the reasons of the SPMM development. Section three depicts the SPMM development. The maturity level algorithm is explicated in the next section. Section five explains the evaluation of the SPO, the assessment results are in section six. The conclusion is given in section seven, and the list of literature in section eight