Effects of distributed database modeling on evaluation of transaction rollbacks

Data distribution, degree of data replication, and transaction access patterns are key factors in determining the performance of distributed database systems. In order to simplify the evaluation of performance measures, database designers and researchers tend to make simplistic assumptions about the system. Here, researchers investigate the effect of modeling assumptions on the evaluation of one such measure, the number of transaction rollbacks in a partitioned distributed database system. The researchers developed six probabilistic models and expressions for the number of rollbacks under each of these models. Essentially, the models differ in terms of the available system information. The analytical results obtained are compared to results from simulation. It was concluded that most of the probabilistic models yield overly conservative estimates of the number of rollbacks. The effect of transaction commutativity on system throughput is also grossly undermined when such models are employed

Effects of distributed database modeling on evaluation of transaction rollbacks

Data distribution, degree of data replication, and transaction access patterns are key factors in determining the performance of distributed database systems. In order to simplify the evaluation of performance measures, database designers and researchers tend to make simplistic assumptions about the system. The effect is studied of modeling assumptions on the evaluation of one such measure, the number of transaction rollbacks, in a partitioned distributed database system. Six probabilistic models and expressions are developed for the numbers of rollbacks under each of these models. Essentially, the models differ in terms of the available system information. The analytical results so obtained are compared to results from simulation. From here, it is concluded that most of the probabilistic models yield overly conservative estimates of the number of rollbacks. The effect of transaction commutativity on system throughout is also grossly undermined when such models are employed

A note on the performance analysis of static locking in distributed database systems

Even though transaction deadlocks can severely affect the performance of distributed database systems, many current evaluation techniques ignore this aspect. Shyu and Li proposed an evaluation method which takes deadlocks into consideration. However, their technique is limited to exclusive locking. Using this technique, researchers illustrate the impact of deadlocks in the presence of shared locking on distributed database performance

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds

Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show $\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and SC-RMSProp for which we show logarithmic regret bounds for strongly convex functions. Finally, we demonstrate in the experiments that these new variants outperform other adaptive gradient techniques or stochastic gradient descent in the optimization of strongly convex functions as well as in training of deep neural networks.Comment: ICML 2017, 16 pages, 23 figure

The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds

Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show $\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and SC-RMSProp for which we show logarithmic regret bounds for strongly convex functions. Finally, we demonstrate in the experiments that these new variants outperform other adaptive gradient techniques or stochastic gradient descent in the optimization of strongly convex functions as well as in training of deep neural networks.Comment: ICML 2017, 16 pages, 23 figure

Performance analysis of static locking in replicated distributed database systems

Data replication and transaction deadlocks can severely affect the performance of distributed database systems. Many current evaluation techniques ignore these aspects, because it is difficult to evaluate through analysis and time consuming to evaluate through simulation. A technique is used that combines simulation and analysis to closely illustrate the impact of deadlock and evaluate performance of replicated distributed database with both shared and exclusive locks

Lower bounds on the obstacle number of graphs

Given a graph $G$, an {\em obstacle representation} of $G$ is a set of points in the plane representing the vertices of $G$, together with a set of connected obstacles such that two vertices of $G$ are joined by an edge if and only if the corresponding points can be connected by a segment which avoids all obstacles. The {\em obstacle number} of $G$ is the minimum number of obstacles in an obstacle representation of $G$. It is shown that there are graphs on $n$ vertices with obstacle number at least $\Omega({n}/{\log n})$