Data visualization is a common and effective technique for data exploration. However, for complex data, it is infeasible for an analyst to manually generate and browse all possible visualizations for insights. This observation motivated the need for automated solutions that can effectively recommend such visualizations. The main idea underlying those solutions is to evaluate the utility of all possible visualizations and then recommend the top-k visualizations. This process incurs high data processing cost, that is further aggravated by the presence of numerical dimensional attributes. To address that challenge, we propose novel view recommendation schemes, which incorporate a hybrid multi-objective utility function that captures the impact of numerical dimension attributes. Our first scheme, Multi-Objective View Recommendation for Data Exploration (MuVE), adopts an incremental evaluation of our multi-objective utility function, which allows pruning of a large number of low-utility views and avoids unnecessary objective evaluations. Our second scheme, upper MuVE (uMuVE), further improves the pruning power by setting the upper bounds on the utility of views and allowing interleaved processing of views, at the expense of increased memory usage. Finally, our third scheme, Memory-aware uMuVE (MuMuVE), provides pruning power close to that of uMuVE, while keeping memory usage within a pre-specified limit.

To support effective data exploration, there is a well-recognized need for solutions that can automatically recommend interesting visualizations, which reveal useful insights into the analyzed data. However, such visualizations come at the expense of high data processing costs, where a large number of views are generated to evaluate their usefulness. Those costs are further escalated in the presence of numerical dimensional attributes, due to the potentially large number of possible binning aggregations, which lead to a drastic increase in the number of possible visualizations. To address that challenge, in this paper we propose the MuVE scheme for Multi-Objective View Recommendation for Visual Data Exploration. MuVE introduces a hybrid multi-objective utility function, which captures the impact of binning on the utility of visualizations. Consequently, novel algorithms are proposed for the efficient recommendation of data visualizations that are based on numerical dimensions. The main idea underlying MuVE is to incrementally and progressively assess the different benefits provided by a visualization, which allows an early pruning of a large number of unnecessary operations. Our extensive experimental results show the significant gains provided by our proposed scheme.

From the security perspective Mobile Ad hoc Networks (MANETs) are amongst the most challenging research areas and one of the key reasons for this is the ambiguous nature of insider attacks in these networks. In recent years, many attempts have been made to study the intrinsic attributes of these insider attacks but the focus has generally been on the analysis of one or very few particular attacks, or only the survey of various attacks without any performance analysis. Therefore, a major feature that research has lately lacked is a detailed and comprehensive study of the effects of various insider attacks on the overall performance of MANETs. In this paper we investigate, in detail, some of the most severe attacks against MANETs namely the blackhole attack, sinkhole attack, selfish node behavior, RREQ flood, hello flood, and selective forwarding attack. A detailed NS-2 implementation of launching these attacks successfully using Ad hoc On-Demand Distance Vector (AODV) routing protocol has been presented and a comprehensive and comparative analysis of these attacks is performed. We use packet efficiency, routing overhead, and throughput as our performance metrics. Our simulation-based study shows that flooding attacks like RREQ flood and hello flood drastically increase the routing overhead of the protocol. Route modification attacks such as sinkhole and blackhole are deadly and severely affect the packet efficiency and bring down the throughput to unacceptable ranges.