Novel graph analytics for enhancing data insight

Graph analytics is a fast growing and significant field in the visualization and data mining community, which is applied on numerous high-impact applications such as, network security, finance, and health care, providing users with adequate knowledge across various patterns within a given system. Although a series of methods have been developed in the past years for the analysis of unstructured collections of multi-dimensional points, graph analytics has only recently been explored. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the graph mining algorithms, but also producing effective graph visualizations in order to enhance human perception. The current thesis deals with the investigation of novel methods for graph analytics, in order to enhance data insight. Towards this direction, the current thesis proposes two methods so as to perform graph mining and visualization. Based on previous works related to graph mining, the current thesis suggests a set of novel graph features that are particularly efficient in identifying the behavioral patterns of the nodes on the graph. The specific features proposed, are able to capture the interaction of the neighborhoods with other nodes on the graph. Moreover, unlike previous approaches, the graph features introduced herein, include information from multiple node neighborhood sizes, thus capture long-range correlations between the nodes, and are able to depict the behavioral aspects of each node with high accuracy. Experimental evaluation on multiple datasets, shows that the use of the proposed graph features for the graph mining procedure, provides better results than the use of other state-of-the-art graph features. Thereafter, the focus is laid on the improvement of graph visualization methods towards enhanced human insight. In order to achieve this, the current thesis uses non-linear deformations so as to reduce visual clutter. Non-linear deformations have been previously used to magnify significant/cluttered regions in data or images for reducing clutter and enhancing the perception of patterns. Extending previous approaches, this work introduces a hierarchical approach for non-linear deformation that aims to reduce visual clutter by magnifying significant regions, and leading to enhanced visualizations of one/two/three-dimensional datasets. In this context, an energy function is utilized, which aims to determine the optimal deformation for every local region in the data, taking the information from multiple single-layer significance maps into consideration. The problem is subsequently transformed into an optimization problem for the minimization of the energy function under specific spatial constraints. Extended experimental evaluation provides evidence that the proposed hierarchical approach for the generation of the significance map surpasses current methods, and manages to effectively identify significant regions and deliver better results. The thesis is concluded with a discussion outlining the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Environmental, health and safety assessment of post-combustion CO2 capture processes with phase-change solvents

A class of solvents for chemisorption-based CO2 capture, phase-change solvents, promises significant energy reductions due to liquid-liquid phase separation and partial solvent recycling before CO2 desorption. Although energy consumption is a critical aspect of the CO2 capture process sustainability, a holistic evaluation of health, safety, and environmental impacts is required to confirm the beneficial performance of processes employing phase-change solvents compared to conventional alternatives. This study outlines a method for combining the life cycle and environmental, health and safety hazard assessment. The method is applied for the first time on processes employing two different exemplary phase-change solvents, a reference aqueous solution of methylcyclohexylamine (MCA) and a novel mixture of cyclohexylpropane-1,3-diamine (S1N) and dimethylcyclohexylamine (DMCA). The results show that phase-change solvents have the potential to be a better alternative to conventional amine (i.e., MEA) solvent systems due to the reduced reboiler duty and lower impact on the environment. However, additional care might need to be taken to prevent the potential accumulation of the carcinogenic nitrosamines in the system

Emerging Evidence for Neolithic Ithaca

The article presents new evidence for the Neolithic habitation on Ithaca, consisting of ceramic and stone finds, recognised among material from the University of Ioannina excavations at the site of Agios Athanasios-School of Homer in the northwest part of Ithaca, Greece. The new Neolithic site is considered within the wider cultural context of the Ionian Islands, in the late 5th/4th millennium BC. Our current knowledge suggests a permanent Neolithic occupation at the site, as opposed to seasonal occupation or to a special purpose occupation. The Neolithic people at the School of Homer may have been part of a dynamic network of Late/Final Neolithic installations in Western Greece, engaging themselves in inter-regional communal connections along the routes of the most ancient seafaring in the Ionian Sea

Secure Computation in Online Social Networks

Apart from their numerous other benefits, online social networks (OSNs) allow users to jointly compute on each other’s data (e.g., profiles, geo-locations, medical records, etc.). Privacy issues naturally arise in this setting due to the sensitive nature of the exchanged information. Ideally, nothing about a user’s data should be revealed to the OSN provider or non-friend users, and even her friends should only learn the output of a joint computation. In this work we propose the first security framework to capture these strong privacy guarantees for general-purpose computation. We also achieve two additional attractive properties: users do not need to be online while their friends compute on their data, and any user value uploaded at the server can be repeatedly used in multiple computations. We formalize our framework in the setting of secure multi-party computation (MPC) and provide two instantiations: the first is a non-trivial adaptation of garbled circuits that converts inputs under different keys to ones under the same key, and the second is based on two-party mixed protocols and involves a novel two-party re-encryption module. We experimentally validate the efficiency of our instantiations using state-of-the-art tools for two concrete use-cases

An entity evolving into a community: defining the common ancestor and evolutionary trajectory of chronic lymphocytic leukemia stereotyped subset #4.

Work was completed during previous post-I joined DMU on January 2016Patients with chronic lymphocytic leukemia (CLL) assigned to stereotyped subset #4 express highly homologous B-cell receptor immunoglobulin (BcR IG) sequences with intense intraclonal diversification (ID) in the context of ongoing somatic hypermutation (SHM). Their remarkable biological and clinical similarities strongly support derivation from a common ancestor. We here revisited ID in subset #4 CLL to reconstruct their evolutionary history as a community of related clones. To this end, using specialized bioinformatics tools we assessed both IGHV-IGHD-IGHJ rearrangements (n = 511) and IGKV-IGKJ rearrangements (n = 397) derived from eight subset #4 cases. Due to high sequence relatedness, a number of subclonal clusters from different cases lay very close to one another, forming a core from which clusters exhibiting greater variation stemmed. Minor subclones from individual cases were mutated to such an extent that they now resembled the sequences of another patient. Viewing the entire subset #4 data set as a single entity branching through diversification enabled inference of a common sequence representing the putative ancestral BcR IG expressed by their still elusive common progenitor. These results have implications for improved understanding of the ontogeny of CLL subset #4, as well as the design of studies concerning the antigenic specificity of the clonotypic BcR IGs

Practical Private Range Search in Depth

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on “practical” private range search (mainly in database venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees or permit unacceptable privacy leakages. In this article, we take an interdisciplinary approach, which combines the rigor of security formulations and proofs with efficient data management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE), primarily proposed for keyword search. We reduce range search to multi-keyword search using range-covering techniques with tree-like indexes, and formalize the problem as Range Searchable Symmetric Encryption (RSSE). We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure and (ii) minimizing false positives incurred by some schemes under heavy data skew. We also explain an important concept in the recent SSE bibliography, namely locality, and design generic and specialized ways to attribute locality to our RSSE schemes. Moreover, we are the first to devise secure schemes for answering range aggregate queries, such as range sums and range min/max. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality