High test data volume and long test application time are two major concerns for testing scan based circuits. The Illinois Scan (ILS) architecture has been shown to be effective in addressing both these issues. The ILS achieves a high degree of test data compression thereby reducing both the test data volume and test application time. The degree of test data volume reduction depends on the fault coverage achievable in the broadcast mode. However, the fault coverage achieved in the broadcast mode of ILS architecture depends on the actual configuration of individual scan chains, i.e., the number of chains and the mapping of the individual flip-flops of the circuit to the respective scan chain positions. Current methods for constructing scan chains in ILS are either ad-hoc or use test pattern information from an a-priori automatic test pattern generation (ATPG) run. In this thesis, we present novel low cost techniques to construct ILS scan configuration for a given design. These techniques efficiently utilize the circuit topology information and try to optimize the flip-flop assignment to a scan chain location without much compromise in the fault coverage in the broadcast mode. Thus, they eliminate the need of an a-priori ATPG run or any test set information. In addition, we also propose a new scan architecture which combines the broadcast mode of ILS and Random Access Scan architecture to enable further test volume reduction on and above effectively configured conventional ILS architecture using the aforementioned heuristics with reasonable area overhead. Experimental results on the ISCAS'89 benchmark circuits show that the proposed ILS configuration methods can achieve on an average 5% more fault coverage in the broadcast mode and on average 15% more test data volume and test application time reduction than existing methods. The proposed new architecture achieves, on an average, 9% and 33% additional test data volume and test application time reduction respectively on top of our proposed ILS configuration heuristics. / Master of Science

This thesis explains Big Data Phenomenon, which is characterised by rapid growth of volume, variety and velocity of data - information assets, and thrives the paradigm shift in analytical data processing. Thesis aims to provide summary and overview with complete and consistent image about the area of High Performance Analytics (HPA), including problems and challenges on the pioneering state-of-art of advanced analytics. Overview of HPA introduces classification, characteristics and advantages of specific HPA method utilising the various combination of system resources. In the practical part of the thesis the experimental assignment focuses on analytical processing of large dataset using analytical platform from SAS Institute. The experiment demonstrates the convenience and benefits of In-Memory Analytics (specific HPA method) by evaluating the performance of different analytical scenarios and operations. Powered by TCPDF (www.tcpdf.org)

This thesis discusses rendering capabilities of web browsers of accelerated 3D scene rendering. It specifically deals with direct volumetric medical data visualization. It focuses on the usage of ray casting algorithm, its quality and its realistic rendering options. One of the goals was to create an application that demonstrates the ability to render three-dimensional volume data in a web browser using WebGL. The application is written in JavaSript and its 3D rendering core uses the Three.js library.

This thesis discusses possibilities of accelerated 3D scene displaying in a Web browser. In more detail, it deals with WebGL standard and its use in real applications. An application for visualization of volumetric medical data based on JavaScript, WebGL and Three.js library was designed and implemented. Image data are loaded from Google Drive cloud storage. An important part of the application is 3D visualization of the volumetric data based on volume rendering technique called Ray-casting.

Med dagens snabba utveckling av informationsteknologin och med ökningen av antalet människor som är uppkopplade mot Internet, blir utvecklingen av webbtjänster allt viktigare. Eftersom webbtjänster har en betydande roll för utvecklingen av Internet, uppstår frågan om vilka verktyg som bör användas för att uppnå den prestanda som dagens användare kräver. Ett vanligt tillvägagångssätt för implementering av webbtjänster är med arkitekturen REST. Dock har REST prestandasvagheter som overfetching, underfetching och hantering av slutpunkter som uppstår i fall där flera slutpunkter nås. Ett alternativ till REST är frågespråket GraphQL som utvecklades för att utesluta de svagheter som REST har och således förbättra prestanda vid datahämtning. I detta arbete utfördes prestandamätningar där latens och datavolym mättes vid olika typer av frågor för respektive GraphQL, REST utan cache och REST med cache. Latens är tidsintervallet från att en klient skickar en förfrågan till att klienten mottar svaret, och datavolym avser storleken på data i ett svarspaket som överförs från en server till en klient. REST med cache togs med i prestandamätningarna då det inte har undersökts i tidigare arbeten som jämfört prestanda mellan GraphQL och REST. Resultaten visade att GraphQL presterar bättre med avseende på både latens och datavolym, i jämförelse med de övriga systemen i fall där förfrågningar ställs mot två eller flera slutpunkter i REST. GraphQL presterade sämre än övriga system, med avseende på latens, när endast en slutpunkt i REST kontaktades. Däremot presterade GraphQL bättre än de övriga systemen, med avseende på datavolym, i samtliga fall. Vid jämförelse av REST med och utan cache visade det sig att ju fler slutpunkter som kontaktades, desto bättre presterade REST utan cache med avseende på datavolym medan REST med cache presterade bättre med avseende på latens. / With today’s rapid development of information technology and with the increase in the number of people connected to the Internet, the development of web services is becoming more important. As web services play a significant role in the development of the Internet, the question arises as to which tools should be used to achieve the performance required by today’s users. A common approach for implementing web services is with the architecture REST. However, REST has performance weaknesses such as overfetching, underfetching, and maintenance of endpoints, that arise in cases where multiple endpoints are accessed. An alternative to REST is the GraphQL query language, which was developed to exclude the weaknesses that REST has and thus improve performance in data retrieval. In this work, performance measurements were conducted where latency and data volume were measured for different types of queries for GraphQL, REST without cache and, REST with cache. Latency is the time interval between a client sending a request and the client receiving the response, and data volume refers to the size of data in a response packet that is transmitted from a server to a client. REST with cache was included in the experiment as it has not been investigated in previous work comparing performance between GraphQL and REST. The results showed that GraphQL performs better, in terms of both latency and data volume, compared to the other systems in cases where requests are made to two or more endpoints in REST. GraphQL performed worse than the other systems, in terms of latency, when only one endpoint in REST was contacted. However, GraphQL performed better than the other systems in terms of data volume in all cases. When comparing REST with and without cache, it turned out that the more endpoints that were contacted, the better REST without cache performed in terms of data volume, while REST with cache performed better in terms of latency.

The diploma thesis is focused on dashboards analysis and distribution and theirs implementation afterwards in SAP Dashboards and Web Intelligence tools. The main goal of this thesis is an analysis of dashboards for different area of company management according to chosen of architecture solution. Another goal of diploma thesis is to take into account the principles of dashboards within the company and it deals with indicator comparison as well. The author further defines data life cycle within Business Intelligence and deals with the decomposition of particular dashboard types in theoretical part. At the end of theory, it is included an important chapter from point of view data quality, data quality process and data quality improvement and an using of SAP Best Practices and KBA as well for BI tools published by SAP. The implementation of dashboards should be back up theoretical part. Implementation is divided into 3 chapters according to selected architecture, using multisource systems, SAP Infosets/Query and using Data Warehouse or Data Mart as an architecture solution for reporting purposes. The deep implementing section should be help reader to make his own opinion to different architecture, but especially difference in used BI tools within SAP Business Objects. At the end of each section regarding architecture and its solution, there are defined pros and cons.