Firat Tekiner

On MANET Routing Protocols for Mobility and Scalability

This paper focuses on performance investigation of reactive and proactive MANET routing protocols, namely AODV, DSR, TORA and OLSR. MANET is a type of Ad Hoc network, and here its functionality is based on 802.11 IEEE standards to communicate in a discrete and disperse environment with no central management [21]. Hence, the main investigation done in this paper is of the discrete feature and routing in MANET. The main issue of MANET is the breakage of link at certain moment and re-generation of link at certain state as it consists of routers which are mobile in nature i. e. are independent to roam in an arbitrary motion. Therefore, this paper presents a performance comparison of the selected MANET routing protocols in a varying network sizes with increasing area and nodes size to investigate mobility and scalability of the routing process.

Big Data Framework

We are constantly being told that we live in the Information Era - the Age of BIG data. It is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is, current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Paper addresses this lacunae by characterising the facets of Big Data and proposing a framework in which Big Data applications can be developed. The framework consists of three Stages and seven Layers to divide Big Data application into modular blocks. The aim is to enable organizations to better manage and architect a very large Big Data application to gain competitive advantage by allowing management to have a better handle on data processing.

A Parallel Distributed Weka Framework for Big Data Mining Using Spark

Effective Big Data Mining requires scalable and efficient solutions that are also accessible to users of all levels of expertise. Despite this, many current efforts to provide effective knowledge extraction via large-scale Big Data Mining tools focus more on performance than on use and tuning which are complex problems even for experts. Weka is a popular and comprehensive Data Mining workbench with a well-known and intuitive interface, nonetheless it supports only sequential single-node execution. Hence, the size of the datasets and processing tasks that Weka can handle within its existing environment is limited both by the amount of memory in a single node and by sequential execution. This work discusses DistributedWekaSpark, a distributed framework for Weka which maintains its existing user interface. The framework is implemented on top of Spark, a Hadoop-related distributed framework with fast in-memory processing capabilities and support for iterative computations. By combining Wekas usability and Sparks processing power, DistributedWekaSpark provides a usable prototype distributed Big Data Mining workbench that achieves near-linear scaling in executing various real-world scale workloads - 91.4% weak scaling efficiency on average and up to 4x faster on average than Hadoop.

Highly scalable Text Mining - parallel tagging application

There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle exponential growth in text data. Problem sizes are increasing by the day by addition of new text docments. The task of labelling sequence data such as part-of-speech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in Text Mining. Genia is a POS tagger which is specifically tuned for biomedical text. Genia is built with maximum entropy modelling and state of the art tagging algorithm. A Parallel version of genia tagger application has been implemented and performance has been compared on a number of different architectures. The focus has been particularly on scalability of the application. Scaling of 512 processors has been achieved and a method to scale to 10000 processors is proposed for massively parallel Text Mining applications. The parallel implementation of genia tagger is done using MPI for achieving portable code.

Towards Automatic Memory Tuning for In-Memory Big Data Analytics in Clusters

Hadoop provides a scalable solution on traditional cluster-based Big Data platforms but imposes performance overheads due to only supporting on-disk data. Data Analytic algorithms usually require multiple iterations over a dataset and thus, multiple, slow, disk accesses. In contrast, modern clusters possess increasing amounts of main memory that can provide performance benefits by efficiently using main memory caching mechanisms. Apache Spark is an innovative distributed computing framework that supports in-memory computations. Even though this type of computations is very fast, memory is a scarce resource and this can cause bottlenecks to execution or, even worse, lead to failures. Spark offers various choices for memory tuning but this requires in-depth systems-level knowledge and the choices will be different across various workloads and cluster settings. Generally, the optimal choice is achieved by adopting a trial and error approach. This work describes a first step towards an automated selection mechanism for memory optimization that assesses workload and cluster characteristics and selects an appropriate caching scheme. The proposed caching mechanism decreases execution times by up to 25% compared to the default strategy and reduces the risk of main memory exceptions.

Super Computer Heterogeneous Classifier Meta-Ensembles

This article describes the entry of the Super Computer Data Mining (SCDM) Project to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition. The SCDM project is developing data mining tools for parallel execution on Linux clusters. The code is freely available; please contact the first author for a copy. We combine several classifiers, some of them ensemble techniques, into a heterogeneous meta-ensemble, to produce a probability estimate for each test case. We then use a simple decision theoretic framework to form a classification. The meta-ensemble contains a Bayesian neural network, a learning classifier system (LCS), attribute selection based-ensemble algorithms (Filtered At-tribute Subspace based Bagging with Injected Randomness [FASBIR]), and more well-known classifiers such as logistic regression, Naive Bayes (NB), and C4.5.

Variance Stabilizing Regression Ensembles for Environmental Models

This paper describes linear regression models fitted for the 2006 predictive uncertainty in environmental modelling competition hosted at the WCCI 2006 conference. Entries into this competition are required to produce models of up to four non-linear regression problems. Rather than adopt a complex non-linear modelling technique, our approach is to fit linear models to transformed data, with adaptive methods used for setting parameters and estimating error. This paper describes several techniques popular with statisticians which are less well known in the computational intelligence community, then proposes new ways of using these statistics. We describe standard statistical transformation techniques, Yeo-Johnson and Box-Tidwell, and present stepwise algorithms for using these transformations on large data sets. These stepwise algorithms utilise the Anscombe procedure, runs tests on residuals, the Goldfeld-Quandt procedure and the Kolomogorov-Smirnoff test for normality. We combine these statistics with the transformation procedures to form a piecewise linear approach to environmental modelling.