Blockchain technology is a booming new field in both computer science and economicsand other use cases than cryptocurrencies are on the rise. Permissioned blockchains are oneinstance of the blockchain technique. In a permissioned blockchain the nodes which validatesnew transactions are trusted. Permissioned blockchains and distributed databasesare essentially two different ways for storing data, but how do they compare in terms ofperformance? This thesis compares Hyperledger Fabric to Apache Cassandra in four experimentsto investigate their insert and read latency. The experiments are executed usingDocker on an Azure virtual machine and the studied systems consist of up to 20 logicalnodes. Latency measurements are performed using varying network size and load. Forsmall networks, the insert latency of Cassandra is twice as high as that of Fabric, whereasfor larger networks Fabric has almost twice as high insert latencies as Cassandra. Fabrichas around 40 ms latency for reading data and Cassandra between 150 ms to 250 ms, thusit scales better for reading. The insert latency of different workloads is heavily affected bythe configuration of Fabric and by the Docker overhead for Cassandra. The read latency isnot affected by different workloads for either system.

Keywords Christa Wolf, myth, mythology, myth revision, Cassandra, Medea, social functions of literature Abstract The subject of this thesis is the incorporation of ancient mythology in the story Cassandra and in the novel Medea: A Modern Retelling by Christa Wolf with the backdrop of the author's own understanding of the function of literature. The first part of the work contains a theoretical introduction to the topic of myths and mythology and presents the most important theories about myths and their incorporation in literature. The subsequent part of the work focuses on the understanding of a myth and its function as used by Christa Wolf. The core of this work is an analysis of the literary incorporation of mythological substance in the writings specified above and a comparison with the incorporation of such substance in the works of ancient authors Aeschylus and Euripides, with the aim of showing through what means and what intentions the author has updated and revised the myth. This part of the work also includes an analysis of the poetological concept of Christa Wolf and the related social functions of literature and the author's role.

This paper focuses on NoSQL database systems. These systems currently serve rather as supplement than replacement of relational database systems. The aim of this paper is to compare 4 selected NoSQL database systems (MongoDB, Apache Cassandra, Apache HBase and Redis) with a main focus on performance and scalability. Performance comparison is done using simulated workload in a 4 nodes cluster environment. One relational SQL database is also benchmarked to provide comparison between classic and modern way of maintaining structured data. As the result of comparison I found out that none of these database systems can be labeled as "the best" as each of the compared systems is suitable for different production deployment.

It has become an important part of business development to collect statistical data from online sources. Information about users and how they interact with an online source can help improving the user experience and increasing sales of products. Collecting data about users has many benefits for the business owner, but it also raises privacy issues since more and more information about users are spread over the internet. Tools that collect statistical data from online sources exists, but using such tools gives away the control over the data collected. If a business implements its own analytics system, it is easier to make it more privacy centric and the control over the data collected is kept.  This thesis examines what techniques that are most suitable for a system whose purpose is to collect, store, process, and present large-scale privacy centric data. Research about what technique to use for collecting data and how to keep track of unique users in a privacy centric way has been made as well as research about what database to use that can handle many write requests and store large scale data. A prototype was implemented based on the research, where JavaScript tagging is used to collect data from several online sources and cookies is used to keep track of unique users. Cassandra was chosen as database for the prototype because of its high scalability and speed at write requests. Two versions of the processing of raw data into statistical reports was implemented to be able to evaluate if the data should be preprocessed or if the reports could be created when the user asks for it.   To evaluate the techniques used in the prototype, load tests of the prototype was made where the results showed that a bottleneck was reached after 45 seconds on a workload of 600 write requests per second. The tests also showed that the prototype managed to keep its performance at a workload of 500 write requests per second for one hour, where it completed 1 799 953 requests. Latency tests when processing raw data into statistical reports was also made to evaluate if the data should be preprocessed or processed when the user asks for the report. The result showed that it took around 30 seconds to process 1 200 000 rows of data from the database which is too long for a user to wait for the report. When investigating what part of the processing that increased the latency the most it showed that it was the retrieval of data from the database that increased the latency. It took around 25 seconds to retrieve the data and only around 5 seconds to process it into statistical reports. The tests showed that Cassandra is slow when retrieving many rows of data, but fast when writing data which is more important in this prototype. / Det har blivit en viktig del av affärsutvecklingen hos företag att samla in statistiska data från deras online-källor. Information om användare och hur de interagerar med en online-källa kan hjälpa till att förbättra användarupplevelsen och öka försäljningen av produkter. Att samla in data om användare har många fördelar för företagsägaren, men det väcker också integritetsfrågor eftersom mer och mer information om användare sprids över internet. Det finns redan verktyg som kan samla in statistiska data från online-källor, men när sådana verktyg används förloras kontrollen över den insamlade informationen. Om ett företag implementerar sitt eget analyssystem är det lättare att göra det mer integritetscentrerat och kontrollen över den insamlade informationen behålls. Detta arbete undersöker vilka tekniker som är mest lämpliga för ett system vars syfte är att samla in, lagra, bearbeta och presentera storskalig integritetscentrerad information. Teorier har undersökts om vilken teknik som ska användas för att samla in data och hur man kan hålla koll på unika användare på ett integritetscentrerat sätt, samt om vilken databas som ska användas som kan hantera många skrivförfrågningar och lagra storskaligdata. En prototyp implementerades baserat på teorierna, där JavaScript-taggning används som metod för att samla in data från flera online källor och cookies används för att hålla reda på unika användare. Cassandra valdes som databas för prototypen på grund av dess höga skalbarhet och snabbhet vid skrivförfrågningar. Två versioner av bearbetning av rådata till statistiska rapporter implementerades för att kunna utvärdera om data skulle bearbetas i förhand eller om rapporterna kunde skapas när användaren ber om den. För att utvärdera teknikerna som användes i prototypen gjordes belastningstester av prototypen där resultaten visade att en flaskhals nåddes efter 45 sekunder på en arbetsbelastning på 600 skrivförfrågningar per sekund. Testerna visade också att prototypen lyckades hålla prestandan med en arbetsbelastning på 500 skrivförfrågningar per sekund i en timme, där den slutförde 1 799 953 förfrågningar. Latenstest vid bearbetning av rådata till statistiska rapporter gjordes också för att utvärdera om data ska förbehandlas eller bearbetas när användaren ber om rapporten. Resultatet visade att det tog cirka 30 sekunder att bearbeta 1 200 000 rader med data från databasen vilket är för lång tid för en användare att vänta på rapporten. Vid undersökningar om vilken del av bearbetningen som ökade latensen mest visade det att det var hämtningen av data från databasen som ökade latensen. Det tog cirka 25 sekunder att hämta data och endast cirka 5 sekunder att bearbeta dem till statistiska rapporter. Testerna visade att Cassandra är långsam när man hämtar ut många rader med data, men är snabb på att skriva data vilket är viktigare i denna prototyp.

Background: Apache Cassandra is a highly scalable distributed system that can handle large amounts of data through several nodes / virtual machines grouped together as Apache Cassandra clusters. When one such node in an Apache Cassandra cluster is down, there is a need for a tool or an approach that can identify this failed virtual machine by analyzing the data generated from each of the virtual machines in the cluster. Manual analysis of this data is tedious and can be quite strenuous. Objectives: The objective of the thesis is to identify, build and evaluate a solution that can detect and report the behaviour of the erroneous or failed virtual machine by analyzing the data generated by each virtual machine in an Apache Cassandra cluster. In the study, we analyzed two specific data sources from each virtual machine, i.e., the wait reports and Apache Cassandra statistics, and proposed a tool named AnoDect to realize this objective. The tool has been built using the input provided by the technical support team at Ericsson through interviews and was also evaluated by them to realize its reliability, usability and, usefulness in an industrial setting. Methods: A case study methodology has been piloted at Ericsson and semi-structured interviews have been conducted to identify the key features in the data along with the functionalities AnoDect needs to perform to assist the CIL team (technical support team at Ericsson) to rectify the erroneous virtual machine in the cluster. An experimental evaluation and a static user evaluation have been conducted, as a part of the case study evaluation, where the experimental evaluation is conducted to identify the best technique for AnoDect's anomaly detection in wait reports and the static evaluation has been conducted to evaluate AnoDect for its reliability and usability once it is deployed for use. Results: From the feedback provided by the CIL team through the questionnaire, it has been observed that the results provided by the tool are quite satisfactory, in terms of usability and reliability of the tool.

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Energy conservation is a major concern in todays data centers, which are the 21st century data processing factories, and where large and complex software systems such as distributed data management stores run and serve billions of users. The two main drivers of this major concern are the pollution impact data centers have on the environment due to their waste heat, and the expensive cost data centers incur due to their enormous energy demand. Among the many subsystems of data centers, the storage system is one of the main sources of energy consumption. Among the many types of storage systems, key/value stores happen to be the widely used in the data centers. In this work, I investigate energy saving techniques that enable a consistent hash based key/value store save energy during low activity times, and whenever there is an opportunity to reuse the waste heat of data centers.

Energy conservation is a major concern in todays data centers, which are the 21st century data processing factories, and where large and complex software systems such as distributed data management stores run and serve billions of users. The two main drivers of this major concern are the pollution impact data centers have on the environment due to their waste heat, and the expensive cost data centers incur due to their enormous energy demand. Among the many subsystems of data centers, the storage system is one of the main sources of energy consumption. Among the many types of storage systems, key/value stores happen to be the widely used in the data centers. In this work, I investigate energy saving techniques that enable a consistent hash based key/value store save energy during low activity times, and whenever there is an opportunity to reuse the waste heat of data centers.

Young adult literature (YA) has been stereotypical in many of its portrayals of LGBTQ teens from the 1960s to the early 2000s, but three contemporary YA series--Cassandra Clare's The Mortal Instruments, Sarah Rees Brennan's Demons trilogy, and Holly Black's Modern Faerie Tales--indicate a change toward more nuanced characterizations. Using four categories--scriptedness, context, importance, and sexuality--to determine whether these representations of LGBTQ youth challenge or reiterate older tropes, my analysis indicates that YA has moved toward more complex representations of queerness, yet some normative discursive structures are still at work, such as poisonings or curses, supernatural parallels to coming out, and heteronormative humour. Although representations of queerness have diversified, then, the implicit ideologies in each author's portrayal of queerness demands closer attention.