Elasticsearch and Cassandra are two of the widely used databases today withElasticsearch showing a more recent resurgence due to its unique full text searchfeature, akin to that of a search engine, contrasting with the conventional querylanguage-based methods used to perform data searching and retrieval operations. The demand for more powerful and better performing yet more feature rich andflexible databases has ever been growing. This project attempts to study how the twodatabases perform under a specific workload of 2,000,000 fixed sized logs and underan environment where the two can be compared while maintaining the results of theexperiment meaningful for the production environment which they are intended for. A total of three benchmarks were carried, an Elasticsearch deployment using defaultconfiguration and two Cassandra deployments, a default configuration a long with amodified one which reflects a currently running configuration in production for thetask at hand. The benchmarks showed very interesting performance differences in terms of CPU,memory and disk space usage. Elasticsearch showed the best performance overallusing significantly less memory and disk space as well as CPU to some degree. However, the benchmarks were done in a very specific set of configurations and a veryspecific data set and workload. Those differences should be considered whencomparing the benchmark results.

This paper tests a revenue catering theory under which investors have time-varying demand for revenue growth and managers will cater to this demand by delivering higher revenue when investors place a higher premium on revenue. I document the time-series variation in the "revenue surprise premium" - a proxy for investor demand for revenue growth, where the "revenue surprise premium" is measured as the earnings announcement period stock return response to good news in revenue after controlling for news in earnings. I investigate whether managers cater to the time-varying "revenue surprise premium" by meeting or beating market expectations of revenue. I find evidence consistent with revenue catering behavior. Firms are more likely to meet or beat analyst forecasts of revenue when the previous quarter's revenue surprise premium is high. I also find evidence that firms use aggressive revenue recognition practices when catering to investors. The results are most pronounced among firms in high-tech and health sectors whose revenue surprise premiums are higher relative to other sectors.

Today, when businesses and organizations are generating huge volumes of data; the applications like Web 2.0 or social networking requires processing of petabytes of data. Stock Exchange Systems are among the ones that process large amount of quotes and trades on a daily basis. The limited database storage ability is a major bottleneck in meeting up the challenge of providing efficient access to information. Further to this, varying data are the major source of information for the financial industry. This data needs to be read and written efficiently in the database; this is quite costly when it comes to traditional Relational Database Management System. RDBMS is good for different scenarios and can handle certain types of data very well, but it isn’t always the perfect choice. The existence of innovative architectures allows the storage of large data in an efficient manner. “Not only SQL” brings an effective solution through the provision of an efficient information storage capability. NOSQL is an umbrella term for various new data store. The NOSQL databases have gained popularity due to different factors that include their open source nature, existence of non-relational data store, high-performance, fault-tolerance, and scalability to name a few. Nowadays, NOSQL databases are rapidly gaining popularity because of the advantages that they offer compared to RDBMS. The major aim of this research is to find an efficient solution for storing and processing the huge volume of data for certain variants. The study is based on choosing a reliable, distributed, and efficient NOSQL database at Cinnober Financial Technology AB. The research majorly explores NOSQL databases and discusses issues with RDBMS; eventually selecting a database, which is best suited for financial data management. It is an attempt to contribute the current research in the field of NOSQL databases which compares one such NOSQL database Apache Cassandra with Apache Lucene and the traditional relational database MySQL for financial management. The main focus is to find out which database is the preferred choice for different variants. In this regard, the performance test framework for a selected set of candidates has also been taken into consideration.

Given the importance of historical dividend policy to firms, I investigate whether dividend payers manipulate earnings through real activities to smooth dividend levels and dividend payout ratios. Using Compustat’s Execucomp database, I find evidence that dividend policy impacts both upward and downward real earnings management. I find that payers manipulate earnings upward through real activities to mitigate the shortfall of pre-managed earnings relative to prior year dividends when pre-managed earnings are lower than dividends paid in the prior year, suggesting that dividend levels are an important earnings benchmark. I document a stronger relationship between changes in pre-managed earnings and real earnings management for payers than for non-payers, suggesting that dividend policies impact real earnings management. Consistent with the importance of dividend policy in real earnings management, I show that dividend payers that follow conservative dividend policies manipulate earnings to a greater extent than dividend payers that do not follow conservative dividend policies.

Many hedge funds are believed to yield considerable returns to investors; there is an assumption that suggests hedge funds seem uncorrelated with market fluctuations and have relatively low volatility. In recent years, emerging market hedge funds have experienced a higher capital inflow in periods when the diversification benefits of investing in emerging markets are higher. However, the strategy‟s share of the hedge fund industry‟s total capital flows has decreased significantly during the same periods: this might imply that investors have reallocated capital to other hedge fund strategies. This paper investigates whether emerging markets hedge funds have been as consistent in performance as the benchmark indices by presenting results of comparative analysis of two sample emerging markets hedge fund indices and two standard emerging markets benchmarks performance. The empirical study ranges from the period of January 2006 to December 2010.

Current advancements in nuclear reactor core design are pushing reactor cores towards greater heterogeneity in an attempt to make nuclear power more sustainable in terms of fuel utilization and long-term disposal needs. These new designs are now being limited by the accuracy of the core simulators/methods. Increasing attention has been given to full core transport as the flux module in future core simulators. However, the current transport methods, due to their significant memory and computational time requirements, are not practical for whole core calculations. While most researchers are working on developing new acceleration and phase space parallelization techniques for the current fine mesh transport methods, this dissertation focuses on the development of a practical heterogeneous coarse mesh transport method. In this thesis, a heterogeneous coarse mesh transport method is extended from two to three dimensions in Cartesian geometry and new techniques are developed to reduce the strain on computational resources. The high efficiency of the method is achieved by decoupling the problem into a series of fixed source calculations in smaller sub-volume elements (e.g. coarse meshes). This decoupling lead to shifting the computation time to a priori calculations of response functions in unique sub-volumes in the system. Therefore, the method is well suited for large problems with repeated geometry such as those found in nuclear reactor cores. Even though the response functions can be generated with any available existing fine-mesh (deterministic or stochastic) code, a stochastic method was selected in this dissertation. Previous work in two dimensions used discrete polynomial expansions that are better suited for treating discrete variables found in pure deterministic transport methods. The amount of data needed to represent very heterogeneous problems accurately became quite large making the three dimensional extension impractical. The deterministic method was thus replaced by a stochastic response function generator making the transition to continuous variables fairly simple. This choice also improves the geometry handling capability of the coarse mesh method.

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Nach einer Einfürung in SPEC und deren Berwertungs- verfahren wird die Art und Weise der SPEC-Leistungsmessungen eingehender beleuchtet. Der Hauptteil beinhaltet Durchführung sowie Auswertung von SPEC-Benchmarks an 5 Workstations. Die dabei gewonnenen Ergebnisse werden mit den offiziell verbreiteten SPEC-Werten verglichen und diskutiert.

Infiniband wird zunehmend als Verbindungsnetzwerk für Cluster eingesetzt. Dadurch wird es nötig existierende Bibliotheken für parallele Programmiersprachen an das neue Netzwerk bestmöglich anzupassen. Ein wichtiger Bestandteil paralleler Programmiersprachen sind dabei kollektive Operationen, die es erfordern, eine Nachricht von einem Knoten zu vielen anderen oder auch von vielen Knoten an einen einzelnen zu senden. Um herauszufinden, welche Verbindungsarten und Operationen am besten für diese kollektiven Operationen geeignet sind, wurde ein Benchmark entwickelt. Ziel dieser Studienarbeit ist es, dieses Programm zu erweitern, auf einem Cluster zu testen und die Ergebnisse auszuwerten.

This report describes PlaceMe, a client side, mobile application built on the Android platform that provides personal location-based services such as location reminders, bookmarking, mapping and search nearby. The reminder system allows creating location based reminders, and alerts the user what he needs to do, when he is in the right place to do them. Bookmarking allows the user to virtually “save” places of interest while he is on the move and obtain driving directions. Mapping enables the user to visualize his relative geographic location in real time, and map the location reminders and bookmarks. Finally, search nearby exploits Google’s powerful local search engine to allow finding and bookmarking nearby places such as gas stations, restaurants, etc, and retrieving map-based directions. We first discuss the requirements and use-cases for PlaceMe, followed an introduction to the Android software stack. Next, we describe our design architecture, implementation model, test strategy and key performance enhancements. Then, we evaluate and compare the performance of the Android platform across a set of standard micro-benchmarks. Finally, we conclude with a discussion of future development ideas and present our thoughts on prospects of app-based mobile computing. / text