Search engines have to deal with a huge amount of data in scalable and efficient ways to produce effective search results. In this paper, we address the problem of building an efficient and scalable experimental search engine over Spark, an in-memory distributed big data processing framework. The proposed system, SparkIR, can serve as a research framework for conducting information retrieval (IR) experiments. SparkIR supports document-based partitioning scheme for indexing and document-at-a-time (DAAT) for query evaluation. Moreover, it offers static pruning (using champion list) to improve the retrieval efficiency. We evaluated the performance of SparkIR using ClueWeb12-B13 collection that contains about 50M English Web pages. Experiments over different subsets of the collection showed that SparkIR exhibits reasonable efficiency and scalability performance overall for both indexing and retrieval.

Cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in telecommunication industry. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) are being widely deployed and utilized by end users, including many private as well as public organizations. Despite its wide-spread acceptance, security is still the biggest threat in cloud computing environments. Users of cloud services are under constant fear of data loss, security breaches, information theft and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning (ML) techniques. In this work, we explore applicability of two well-known machine learning approaches, which are, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to detect intrusions or anomalous behavior in the cloud environment. We have developed ML models using ANN and SVM techniques and have compared their performances. We have used UNSW-NB-15 dataset to train and test the models. In addition, we have performed feature engineering and parameter tuning to find out optimal set of features with maximum accuracy to reduce the training time and complexity of the ML models. We observe that with proper features set, SVM and ANN techniques have been able to achieve anomaly detection accuracy of 91% and 92% respectively, which is higher compared against that of the one achieved in the literature, with reduced number of features needed to train the models.

The increase in biomedical data has given rise to the need for developing data sampling techniques. With the emergence of big data and the rise of popularity of data science, sampling or reduction techniques have been assistive to significantly hasten the data analytics process. Intuitively, without sampling techniques, it would be difficult to efficiently extract useful patterns from a large dataset. However, by using sampling techniques, data analysis can effectively be performed on huge datasets, to produce a relatively small portion of data, which extracts the most representative objects from the original dataset. However, to reach effective conclusions and predictions, the samples should preserve the data behavior. In this paper, we propose a unique data sampling technique which exploits the notion of formal concept analysis. Machine learning experiments are performed on the resulting sample to evaluate quality, and the performance of our method is compared with another sampling technique proposed in the literature. The results demonstrate the effectiveness and competitiveness of the proposed approach in terms of sample size and quality, as determined by accuracy and the F1-measure.

Authenticated key agreement protocols play a significant role in securing communications over public network channels (Internet).This paper proposes a new key agreement protocol based on factorization problem over nonabelian groups.Then it presents two different ways to provide mutual authentication for the proposed protocol; this paper presents a new authenticated key agreement protocol using fixed shared password and a new authenticated key agreement protocol using a digital signature.It also provides security analysis for the proposed two authenticated key agreement protocols.