Measurement and evaluation of academic performance is an highly debated research area and results of the studies in this area are closely followed by a large segment of the society. In general, researches conducted in this domain evaluate higher education institutions as a whole, but such an approach actually represents an average performance of the research fields, which are actively studied by the members of institutions. This may be misleading, because academic performance varies for each university depending on the field of research. However, people who are interested in the results of these studies require more detailed information about field based academic performances of institutions. One of these studies mentioned above have been implemented in 2011 by University Ranking by Academic Performance (URAP) research laboratory which was established in Middle East Technical University - Informatics Institute. In this study, 2000 universities around the world have been ranked according to multiple criteria in terms of overall academic performance. Interests shown to results of the system implemented by URAP revealed a need for a more comprehensive ranking system, which deals with the evaluation of field based academic performance. In this sense, within the scope of this study, universities ranked by URAP research laboratory were evaluated in terms of their academic performance in the following six research fields / Agriculture &amp / Environmental Sciences (AGE) Clinical Medicine (MED) Engineering, Computing &amp / Technology (ENG) Life Sciences (LIFE) Natural Sciences (SCI) Social Sciences (SOC) Institutions in this study has been evaluated according to data that have been collected from ISI - Web of Knowledge for the indicators listed below. Article Count (last year) Total Document Count (last 5 years) Cumulative Journal Impact (last 5 years) Total Citation Count (last 5 years) H-Index (average of last 5 years) The results indicate that status of universities from the point of academic performance varies according to the research field.

This study was designed to investigate Plantago major leaves, which is widely used in ethnobotanical uses in Turkey as is the case in all over the world, for their antioxidant capacities and antimicrobial effect on two gram-positive and two gramnegative bacteria. Extracts of P. major leaves prepared in the methanol and fraction samples were collected in solvents in increasing polarity, from non-polar to polar, to separate bioactive compound in crude extract. Antioxidant capacities of the crude extracts and fractions were examined by radical scavenging methods, namely 2,2-azinobis(3- ethylbenzothiazoline-6-sulfonic acid) ABTS and 1,1-diphenyl-2-picryl-hydrazyl (DPPH) / as well, total phenolic and total flavonoid contents were determined. Furthermore, antimicrobial activities of crude extract and fractions were calculated in terms of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) methods together with disc diffusion method. v Among the investigated samples, ethyl acetate phase had the highest radical scavenging capacity with the ABTS value of 2130.4&plusmn / 9.845 &mu / mol equivalents of trolox/ g of ethyl acetate fraction DW, and with EC50 value of 0.011&plusmn / 0.001 mg/mL / further, the highest phenolic content was found in ethyl acetate phase with values of 0.452 GAE mg/mg of ethyl acetate fraction DW and 0.434&plusmn / 0.0017 mg catechin equivalent in per mg of ethyl acetate fraction DW. In general, it was found that the crude extract and aqueous phase of Plantago major had a weak antimicrobial effect on S. aureus, P.mirabilis, S.pyogenes and E. coli, along with the highest bacteriostatic and bactericidal upshot on selected bacterial populations in ethyl acetate fraction.

Mit dem Übergang zum digitalen Medium haben sich die Bezugsstrukturen und das Angebot an elektronischer Fachinformation in den Bibliotheken nachhaltig verändert. In den vorliegenden Untersuchungen wird das Angebot elektronischer Zeitschriften und Datenbanken und die Nutzung elektronischer Zeitschriften in fünf ausgewählten Fachgebieten und in unterschiedlichen Bibliothekstypen dargelegt. Darüber hinaus werden die derzeitigen Bezugsstrukturen beschrieben sowie die Ergebnisse einer Befragung der Konsortien zu Zielsetzungen, Vertragsformen und Geschäftsmodellen dargestellt. Chancen und Risiken der konsortialen Bezugsform werden erörtert. / With the transition to the digital medium the structures for purchasing digital information and the offer of scientific information in the libraries changed strongly. In the available examination the offer of electronic journals and databases and the usage of electronic journals in five selected disciplines and in different types of libraries is evaluated. Further more the current purchasing structures and the results of interviews with consortia in regard to objectives, forms of contracts and pricing models are described. Chances and risks of consortia purchasing are discussed.

ABSTRACT Broadcasting data delivery is rapidly becoming the good choice for disseminating information to a massive user population in many new application areas where the client-to-server communication is limited. There are two different ways of data dissemination. One is called push-based that the data items are broadcasted periodically in the channels, another one is called pull-based that the client requests a piece of data on the uplink channel and the server responds by sending this piece of data to the client. In push-based, most of the previous researches assume that each mobile client needs only one data item. However, in many situations, a mobile client might need more than one data item. In pull-based, the data items were broadcasted dynamically. Most of the previous researches assume that the data items which requested by the clients are of the same size. However, the data items may of different sizes in reality. In this thesis, we propose Improved QDS Expansion Method (Improved-QEM) and Heuristic On-line Algorithm to overcome the above two weaknesses, respectively. The issue of scheduling the broadcast data for the situation that each client may access multiple data items can not be simply considered as multiple subissues. There have been two methods was proposed, Query Expansion Method (QEM) and Modified Query Expansion Methods (Modified-QEM). These two methods are heuristic-based algorithm and do not provide the optimal solution. To improve the performance, our Improved-QEM is an efficient scheduling for query-set-based broadcasting, which is integrated with Query Expansion Method (QEM) and mining association rules technique. The mining association rules can globally find the data item sets (large itemsets) which are requested by clients, frequently. From our simulation results, we show that, as compared to the local optimal approach in the previous methods, our Improved-QEM can construct the schedule with the smaller TQD than that constructed by QEM and Modified-QEM, where TQD is denotes Total Query Distance and is proportional to the average access time. The on-line (push-based) algorithms are easy to adapt to time varying demands for the data items, which uses some decision-making mechanism to determine which data item is to be broadcasted next. Hence, when the number of data items is huge, it is important to schedule broadcasting program such that, it can provide the small overall mean access time. Therefore, Vaidya and Hameed have proposed two on-line algorithms, On-line Algorithm and On-line with Bucketing Algorithm. The main disadvantage of On-line Algorithm is the heavy run-time overhead and the main disadvantage of On-line Algorithm with Bucketing is the poor performance of the overall mean access time. Therefore, we propose the heuristic on-line algorithm to solve these two problems. From our simulation results, we show that our heuristic algorithm provides the performance that closes to the overall mean access time and has with low run-time overhead.

An efficient method to compute local density-based outliers in high dimensional data was proposed. In our work, we have shown that this type of outlier is present even in any subset of the dataset. This property is used to partition the data set into random subsets to compute the outliers locally. The outliers are then combined from different subsets. Therefore, the local density-based outliers can be computed efficiently. Another challenge in outlier detection in high dimensional data is that the outliers are often suppressed when the majority of dimensions do not exhibit outliers. The contribution of this work is to introduce a filtering method whereby outlier scores are computed in sub-dimensions. The low sub-dimensional scores are filtered out and the high scores are aggregated into the final score. This aggregation with filtering eliminates the effect of accumulating delta deviations in multiple dimensions. Therefore, the outliers are identified correctly. In some cases, the set of outliers that form micro patterns are more interesting than individual outliers. These micro patterns are considered anomalous with respect to the dominant patterns in the dataset. In the area of anomalous pattern detection, there are two challenges. The first challenge is that the anomalous patterns are often overlooked by the dominant patterns using the existing clustering techniques. A common approach is to cluster the dataset using the k-nearest neighbor algorithm. The contribution of this work is to introduce the adaptive nearest neighbor and the concept of dual-neighbor to detect micro patterns more accurately. The next challenge is to compute the anomalous patterns very fast. Our contribution is to compute the patterns based on the correlation between the attributes. The correlation implies that the data can be partitioned into groups based on each attribute to learn the candidate patterns within the groups. Thus, a feature-based method is developed that can compute these patterns efficiently.

State monitoring is a fundamental building block for Cloud services. The demand for providing state monitoring as services (MaaS) continues to grow and is evidenced by CloudWatch from Amazon EC2, which allows cloud consumers to pay for monitoring a selection of performance metrics with coarse-grained periodical sampling of runtime states. One of the key challenges for wide deployment of MaaS is to provide better balance among a set of critical quality and performance parameters, such as accuracy, cost, scalability and customizability. This dissertation research is dedicated to innovative research and development of an elastic framework for providing state monitoring as a service (MaaS). We analyze limitations of existing techniques, systematically identify the need and the challenges at different layers of a Cloud monitoring service platform, and develop a suite of distributed monitoring techniques to support for flexible monitoring infrastructure, cost-effective state monitoring and monitoring-enhanced Cloud management. At the monitoring infrastructure layer, we develop techniques to support multi-tenancy of monitoring services by exploring cost sharing between monitoring tasks and safeguarding monitoring resource usage. To provide elasticity in monitoring, we propose techniques to allow the monitoring infrastructure to self-scale with monitoring demand. At the cost-effective state monitoring layer, we devise several new state monitoring functionalities to meet unique functional requirements in Cloud monitoring. Violation likelihood state monitoring explores the benefits of consolidating monitoring workloads by allowing utility-driven monitoring intensity tuning on individual monitoring tasks and identifying correlations between monitoring tasks. Window based state monitoring leverages distributed windows for the best monitoring accuracy and communication efficiency. Reliable state monitoring is robust to both transient and long-lasting communication issues caused by component failures or cross-VM performance interferences. At the monitoring-enhanced Cloud management layer, we devise a novel technique to learn about the performance characteristics of both Cloud infrastructure and Cloud applications from cumulative performance monitoring data to increase the cloud deployment efficiency.

Im Rahmen dieser Dissertation wird sich mit der Einbettung komplexer Datenanalyseprozesse in serviceorientierten Umgebungen beschäftigt. Diese Betrachtung beginnt mit einem konkreten Anwendungsgebiet, indem derartige Analyseprozesse eine entscheidende Rolle bei der Wissenserschließung spielen und ohne deren Hilfe kein Fortschritt erzielt werden kann. Im zweiten Teil werden konkrete komplexe Datenanalyseprozesse entwickelt, die den Ausgangspunkt für die Erörterung der Einbettung in eine serviceorientierte Umgebung bilden. Auf diese Einbettung wird schlussendlich im dritten Teil der Dissertation eingegangen und entsprechende Erweiterungen an den Technologien der bekanntesten Realisierungsform präsentiert. In der Evaluierung wird gezeigt, dass diese neue Form wesentlich besser geeignet ist für komplexe Datenanalyseprozesse als die bisherige Variante.

<p>Distributed real-time databases can be used to support data sharing</p><p>for applications in wireless ad-hoc networks. In such networks, topology changes frequently and partitions may be unpredictable and last for an unbounded period. In this thesis, the existing database replication protocol PRiDe is extended to handle such long-lasting partitions. The protocol uses optimistic and detached replication to provide predictable response times in unpredictable networks and forward conflict resolution to guarantee progress.</p><p>The extension, pPRiDe, combines update and state transfer strategies. Update transfer for intra-partition communication can reduce bandwidth usage and ease conflict resolution. State transfer for inter partition conflicts removes dependency on a common state between partitions prior to the merge to apply update messages on. This makes the resource usage independent of the life span of partitions. This independence comes at the cost of global data stability guarantees and pPRiDe can thus only provide per partition guarantees. The protocol supports application specific conflict resolution routines for both</p><p>state and update conflicts. A basic simulator for mobile ad-hoc networks has been developed to validate that pPRiDe provides eventual consistency.</p><p>pPRiDe shows that a hybrid approach to change propagation strategy can be beneficial in networks where collaboration by data sharing within long lasting partitions and predictable resource usage is necessary. These types of systems already require the conflict management routines necessary for pPRiDe and can benefit from an existing protocol.</p><p>In addition to pPRiDe and the simulator this thesis provides a flexible object database suitable for future works and an implementation of PRiDe on top of that database.</p>

<p>In this dissertation, we consider the problem of recovering a crashed node in a distributed database. We especially focus on real-time recovery in eventually consistent databases, where the consistency of replicated data is traded off for increased predictability, availability and performance. To achieve this focus, we consider consistency preservation techniques as well as recovery mechanisms.</p><p>Our approach is to perform a thorough literature survey of these two fields. The literature survey considers not only recovery in real-time, distributed, eventually consistent databases, but also related techniques, such as recovery in main-memory resident or immediately consistent databases. We also examine different techniques for consistency preservation.</p><p>Based on this literature survey, we present a taxonomy and state-of-the-art report on recovery mechanisms and consistency preservation techniques. We contrast different recovery mechanisms, and highlight properties and aspects of these that make them more or less suitable for use in an eventually consistent database. We also identify unexplored areas and uninvestigated problems within the fields of database recovery and consistency preservation. We find that research on real-time recovery in distributed databases is lacking, and we also propose further investigation of how the choice of consistency preservation technique affects (or should affect) the design of a recovery mechanism for the system.</p>

<p>The focus of this dissertation is on large-scale and long-running networked real-time multiplayer games. In this type of games, each player controls one or many entities, which interact in a shared virtual environment. Three attributes - scalability, security, and fault tolerance - are considered essential for this type of games. The normal approaches for building this type of games, using a client/server or peer-to-peer architecture, fail in achieving all three attributes. We propose a server-network architecture that supports these attributes. In this architecture, a cluster of servers collectively manage the game state and each server manages a separate region of the virtual environment. We discuss how the architecture can be extended using proxies, and we compare it to other similar architectures. Further, we investigate how a distributed database management system can support the proposed architecture. Since efficiency is very important in this type of games, some properties of traditional database systems must be relaxed. We also show how methods for increasing scalability, such as interest management and dead reckoning, can be implemented in a database system. Finally, we suggest how the proposed architecture can be validated using a simulation of a large-scale game.</p>