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17 Papers

Reducing IoT Big Data for Efficient Storage and Processing

Topics: Internet of Things; Data Processing

Authors: Eleftheria Katsarou and Stathes Hadjiefthymiades

Affiliation: Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimioupolis, Athens, Greece

Digital Object Identifier: 10.5220/0011983900003482

Proceedings of IoTBDS 2023, ISBN: 978-989-758-643-9

Quick Abstract

We focus on the very important problem of managing IoT data. We consider the data gathering process that yields big data intended for CDN/cloud storage. We aim to reduce big data into small data to efficiently exploit available storage without compromising their usability and interpretation. This reduction process is to be performed at the edge of the infrastructure (IoT edge devices, CDN edge servers) in a computationally acceptable way. Therefore, we employ reservoir sampling, a method that stochastically samples data and derives synopses that are finally pushed and maintained in the available storage capability. We implemented the discussed architecture using reverse proxy technologies and in particular the Varnish open source server. We provide details of our implementation and discuss critical parameters like the frequency of synopsis generation and CDN/cloud storage.

Rule-based Business Process Abstraction Framework

Authors: Christina Tsagkani ¹ and Aphrodite Tsalgatidou ²

Affiliations: ¹ University of Athens, Greece , ² National and Kapodistrian University of Athens, Greece

Digital Object Identifier: 10.5220/0006223401730178

Proceedings of BMSD 2016, ISBN: 978-989-758-190-8

Quick Abstract

Business process models have become key artifacts to represent how work is performed in organisations. Therefore organisations maintain large process model repositories, containing complex models that are difficult to be managed. Abstraction is a means to reduce the size and complexity of such process models and ease the process model management. It is worthwhile noticing that several abstraction mechanisms exist in the literature but none of them includes activities-paths, data, roles, artifacts and messages in the abstraction procedure. To this end we present a conceptual framework that utilizes abstraction rules in order to simplify business process models. The proposed framework is well suited for complex BPMN process models and is built on our previous work where process abstraction rules had been introduced. This abstraction framework is designed with focus on retaining the overall structure of the process model, but most of all it is designed to treat different process elements (eg. data, messages, roles, etc.) other than activities as abstraction objects. A real business case that is originated from the financial services sector is used as an attempt to exemplify and validate the proposed mechanism.

Artificial Neural Network based Methodologies for the Spatial and Temporal Estimation of Air Temperature - Application in the Greater Area of Chania, Greece

Authors: Despina Deligiorgi , Kostas Philippopoulos and Georgios Kouroupetroglou

Affiliation: University of Athens, Greece

Digital Object Identifier: 10.5220/0004373906690678

Proceedings of PRG 2013/ICPRAM, ISBN: 978-989-8565-41-9

Quick Abstract

Artificial Neural Networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. ANNs can be used as function approximators to estimate both the time and spatial air temperature distributions based on observational data. After reviewing the theoretical background as well as the relative advantages and limitations of ANN methodologies applicable to the field of air temperature time series and spatial modelling, this work focuses on implementation issues and on evaluating the accuracy of the AAN methodologies using a set of metrics in the case of a specific region with complex terrain. A number of alternative feed forward ANN topologies have been applied in order to assess the spatial and time series air temperature prediction capabilities in different horizons. For the temporal forecasting of air temperature ANNs were trained using the Levenberg-Marquardt back propagation algorithm with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. For the spatial estimation of air temperature the Radial Basis Function and Multilayer Perceptrons non-linear Feed Forward AANs schemes are compared. The underlying air temperature temporal and spatial variability is found to be modeled efficiently by the ANNs.

IMPACT OF H.264 ADVANCED VIDEO CODING INTER-FRAME BLOCK SIZES ON VIDEO QUALITY

Topics: Image Enhancement and Restoration; Image and Video Coding and Compression

Authors: Harilaos Koumaras ¹ , Michail-Alexandros Kourtis ² , Drakoulis Martakos ² and Christian Timmerer ³

Affiliations: ¹ NCSR Demokritos, Greece , ² University of Athens, Greece , ³ Alpen-Adria-Universität Klagenfurt, Austria

Digital Object Identifier: 10.5220/0003846401180122

Proceedings of VISAPP 2012/VISIGRAPP, ISBN: 978-989-8565-03-7

Quick Abstract

In this paper, we present a perceptual-based encoding benchmarking of the H.264 Advanced Video Coding (AVC) inter-frame prediction variable block sizes for various spatial and temporal contents. This paper in order to quantify the impact on the video quality of the AVC inter-frame variable block sizes and the responsible prediction algorithm has disabled the motion estimation mechanism of the encoder and manually each block size is selected. Thus each time only one available block size out of the total seven is available to be searched for each MB and it is possible to examine the video quality impact of each block size independently to the remaining ones. The scope of this paper is to study if the use of sophisticated predictions algorithms and variable block sizes enhance the perceived quality of the encoded video signal or if there is not any significant quality degradation when the option of variable size is disabled.

Related Ontology Subjects/Areas/Topics (4)

Computer Vision, Visualization and Computer Graphics		Image Enhancement and Restoration		Image Formation and Preprocessing
Image and Video Coding and Compression

A NOVEL INTRUSION DETECTION SYSTEM FOR MANETS

Topics: Sensor and Mobile Ad Hoc Network Security; Intrusion Detection & Prevention

Authors: Christoforos Panos ¹ , Christos Xenakis ² and Ioannis Stavrakakis ¹

Affiliations: ¹ University of Athens, Greece , ² University of Piraeus, Greece

Digital Object Identifier: 10.5220/0002989100250034

Proceedings of SECRYPT 2010/ICETE, ISBN: 978-989-8425-18-8

Quick Abstract

This paper proposes a novel Intrusion Detection System (IDS) for Mobile Ad Hoc Networks (MANETs) that aims at overcoming the limitations and weaknesses of the existing IDSs. The proposed IDS incorporates a novel random walk-based IDS architecture as well as a multi-layer, specification-based detection engine. The proposed solution does not belong to any of the existing intrusion detection approaches, since it relies on a set of robust, self-contained Random Walk Detectors (RWDs), which may freely move from node to node and randomly traverse a network, while monitoring each visiting node for malicious behaviour. RWDs exhibit a number of benefits including locality, simplicity, low overhead, and robustness to changes in topology. Moreover, the multi-layer, specification-based engine monitors the transport, network and data link layers of the protocol stack, providing an integrated solution capable of detecting the majority of security attacks occurring in MANETs.

Related Ontology Subjects/Areas/Topics (4)

Information and Systems Security		Network Security		Sensor and Mobile Ad Hoc Network Security
Intrusion Detection & Prevention

A REVIEW OF LEARNING METHODS ENHANCED IN STRATEGIES OF NEGOTIATING AGENTS

Topics: Intelligent Agents; Market-Spaces: Market Portals, Hubs, Auctions

Authors: Marisa Masvoula , Panagiotis Kanellis and Drakoulis Martakos

Affiliation: Nationaland Kapodistrian University of Athens, Greece

Digital Object Identifier: 10.5220/0002897102120219

Proceedings of ICEIS 2010, ISBN: 978-989-8425-05-8

Quick Abstract

Advancement of Artificial Intelligence has contributed in the enhancement of agent strategies with learning techniques. We provide an overview of learning methods that form the core of state-of-the art negotiators. The main objective is to facilitate the comprehension of the domain by framing current systems with respect to learning objectives and phases of application. We also aim to reveal current trends, virtues and weaknesses of applied methods.

Related Ontology Subjects/Areas/Topics (9)

Agents	Enterprise Information Systems	Market-Spaces: Market Portals, Hubs, Auctions
Artificial Intelligence	Intelligent Agents	Software Agents and Internet Computing
Artificial Intelligence and Decision Support Systems	Internet Technology	Web Information Systems and Technologies

BUSINESS PROCESS FLEXIBILITY - Evaluation Criteria and Guidelines

Topics: Business-It Alignment; Banking

Author: Christina Tsagkani

Affiliation: University of Athens, Greece

Digital Object Identifier: 10.5220/0002986301790187

Proceedings of ICE-B 2010/ICETE, ISBN: 978-989-8425-17-1

Quick Abstract

The business environment of most enterprises comprises of fluid requirements, and emergent behaviour that cause continuous changes across the enterprises’ business processes. Thus, Process Management Systems (PMSs) able to handle such changes become a necessity for businesses in order to effectively respond in this volatile environment. However, despite the plethora of available PMS, dynamic process change is hardly being addressed in most of them. Therefore, the task of selecting a PMS that supports flexible business processes effectively and in this way face the volatile nature of the business environment is not easy. This task is being addressed in this paper by proposing a set of evaluation criteria for flexible PMS. In addition, a business case scenario from the banking sector and selection guidelines have been employed, in order to demonstrate how the proposed criteria framework may be applied practically during the selection of the ‘best-fit’ PMS.

Related Ontology Subjects/Areas/Topics (6)

Applications		Business-It Alignment		Enterprise Engineering
Banking/Finance		e-Business		Enterprise Information Systems

EVOLVING STRUCTURES FOR PREDICTIVE DECISION MAKING IN NEGOTIATIONS

Topics: Neural Network Software and Applications; B2B and B2C Applications; Intelligent Agents

Authors: Marisa Masvoula , Panagiotis Kanellis and Drakoulis Martakos

Affiliation: National and Kapodistrian University of Athens, Greece

Digital Object Identifier: 10.5220/0002895003910394

Proceedings of ICEIS 2010, ISBN: 978-989-8425-05-8

Quick Abstract

Predictive decision making increases the individual or joint gain of negotiators, and has been extensively studied. One particular skill of predicting agents is the forecast of their opponents’ future offers. Current systems focus on enhancing learning techniques in the decision making module of negotiating agents, with the purpose to develop more robust systems. Empirical studies are conducted in bounded problem spaces, where data distribution is known or assumed. Our proposal concentrates on the incorporation of learning structures in agents’ decision making, capable of forecasting opponents’ future offers even in open problem spaces, which is the case in most negotiation situations.

Related Ontology Subjects/Areas/Topics (30)

Agents	e-Business	Neurotechnology, Electronics and Informatics
Artificial Intelligence	Enterprise Information Systems	Pattern Recognition
Artificial Intelligence and Decision Support Systems	Health Engineering and Technology Applications	Physiological Computing Systems
B2B, B2C and C2C	Human-Computer Interaction	Sensor Networks
B2C/B2B Considerations	Intelligent Agents	Signal Processing
Biomedical Engineering	Internet Technology	Society, e-Business and e-Government
Biomedical Signal Processing	Methodologies and Methods	Soft Computing
Business and Social Applications	Neural Network Software and Applications	Software Agents and Internet Computing
Communication and Software Technologies and Architectures	Neural Networks	Theory and Methods
Computational Intelligence	Neurocomputing	Web Information Systems and Technologies

INTEGRATING AGENTS WITH CONNECTIONIST SYSTEMS TO EXTRACT NEGOTIATION ROUTINES

Topics: Neural Network Software and Applications; Intelligent Agents

Authors: Marisa Masvoula , Panagiotis Kanellis and Drakoulis Martakos

Affiliation: National and Kapodistrian University of Athens, Greece

Digital Object Identifier: 10.5220/0002155302510256

Proceedings of ICEIS 2009, ISBN: 978-989-8111-85-2

Quick Abstract

Routinization is a technique of knowledge exploitation based on the repetition of acts. When applied to negotiations it results the substitution of parts or even whole processes, disembarrassing negotiators from significant deliberation and decision making effort. Although it has an important impact on negotiators, the risk of establishing ineffective routines is evident. In our paper we discuss weaknesses and limitations and we propose a generic framework to address them. We consider routines as evolving processes and we take two orientations. The first concerns a communicative dimension to allow for external evaluation of the applied routines and the second concerns enforcement of the system core with evolving structure that adjusts to routine changes and flexibly incorporates new knowledge.

Related Ontology Subjects/Areas/Topics (23)

Agents	Human-Computer Interaction	Pattern Recognition
Artificial Intelligence	Intelligent Agents	Physiological Computing Systems
Artificial Intelligence and Decision Support Systems	Internet Technology	Sensor Networks
Biomedical Engineering	Methodologies and Methods	Signal Processing
Biomedical Signal Processing	Neural Network Software and Applications	Soft Computing
Computational Intelligence	Neural Networks	Theory and Methods
Enterprise Information Systems	Neurocomputing	Web Information Systems and Technologies
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics

USING STEGANOGRAPHY TO IMPROVE HASH FUNCTIONS’ COLLISION RESISTANCE

Topics: Information Hiding; Data Integrity

Authors: Emmanouel Kellinis ¹ and Konstantinos Papapanagiotou ²

Affiliations: ¹ KPMG LLP, One Canada Square, United Kingdom , ² University of Athens, Greece

Digital Object Identifier: 10.5220/0002125203370340

Proceedings of SECRYPT 2007/ICETE, ISBN: 978-989-8111-12-8

Quick Abstract

Lately, hash function security has received increased attention. Especially after the recent attacks that were presented for SHA-1 and MD5, the need for a new and more robust hash function has become imperative. Even though many solutions have been proposed as replacements, the transition to a new function could be costly and complex. In this paper, we introduce a mode of operation that can be applied to any existing or future hash function in order to improve its collision resistance. In particular, we use steganography, the art of hiding a message into another message, to create a scheme, named Σ-Hash, which enforces the security of hashing algorithms. We will demonstrate how, apart from hash function security, Σ-Hash can also be used for securing Open Source code from tampering attacks and other applications.

Related Ontology Subjects/Areas/Topics (6)