Skip to main content
Disciplines
Automotive
Business IT + Informatics
Construction + Real Estate
Electrical Engineering + Electronics
Energy + Sustainability
Insurance + Risk
Finance + Banking
Management + Leadership
Marketing + Sales
Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Start single access now
Request access for companies
EXTENDED SEARCH
Log in
Top

2023 | Book

Cloud-Based Urban Mobility Services Read chapter Read first chapter

Algorithmic Aspects of Cloud Computing

7th International Symposium, ALGOCLOUD 2022, Potsdam, Germany, September 6, 2022, Revised Selected Papers

Editors: Luca Foschini, Spyros Kontogiannis

Publisher: Springer International Publishing

Book Series : Lecture Notes in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

This book constitutes revised selected papers from the refereed proceedings of the 7th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2022, which took place in Potsdam, Germany, on September 6, 2022.
The 6 full papers included in this book were carefully reviewed and selected from 16 submissions. They were organized in topical sections as follows: Cloud-Based Urban Mobility Services; New Results in Priority-Based Bin Packing; More Sparking Soundex-based Privacy-Preserving Record Linkage and Privacy Preserving Queries of Shortest Path Distances.

Table of Contents

Frontmatter
Cloud-Based Urban Mobility Services
Abstract
We present a cloud-based ecosystem for urban mobility services that involves citizens, authorities, corporations, resources, and services, all working together towards a common well-being. Our goal is to have a platform that allows the exploitation of shared mobility-related data sources by harmonically cooperating mobility-related services, and at the same time smoothly balances the computational load across the full cloud continuum. Towards this goal, we present the relevant orchestration mechanisms, both at service level and at cloud substrate level, which take into account particular characteristics per mobility service, and real-time performance measurements and availability of computational nodes within the cloud substrate. Moreover, our ecosystem allows the migration of both data segments and source-code segments within the cloud infrastructure, towards optimizing an objective for the entire ecosystem’s performance and sustainability. Our core services are based on novel algorithmic approaches that are deemed necessary for providing real-time query responses.
Spyros Kontogiannis, Paraskevi-Maria Machaira, Andreas Paraskevopoulos, Konstantinos Raftopoulos, Christos Zaroliagis
SQL Query Optimization in Distributed NoSQL Databases for Cloud-Based Applications
Abstract
A method for query optimization is presented by utilizing Spark SQL, a module of Apache Spark that integrates relational data processing. The goal of this paper is to explore NoSQL databases and their effective usage in conjunction with distributed environments to optimize query execution time, in order to accommodate the user complex demands in a cloud computing setting that necessitate the real-time generation of dynamic pages and the provision of dynamic information.
In this work, we investigate query optimization using various query execution paths by combining MongoDB and Spark SQL, aiming to reduce the average query execution time. We achieve this goal by improving the query execution time through a sequence of query execution path scenarios that split the initial query into sub-queries between MongoDB and Spark SQL, along with the use of a mediator between Apache Spark and MongoDB. This mediator transfers either the entire database from MongoDB to Spark, or transfers a subset of the results for those sub-queries executed in MongoDB. Our experimental results with eight different query execution path scenarios and six difference database sizes demonstrate the clear superiority and scalability of a specific scenario.
Aristeidis Karras, Christos Karras, Antonios Pervanas, Spyros Sioutas, Christos Zaroliagis
MAGMA: Proposing a Massive Historical Graph Management System
Abstract
In recent years, maintaining the history of graphs has become more and more imperative due to the emergence of related applications in a number of fields like health services, social interactions, and map guidance. Historical graphs focus on being able to store and query the whole evolution of the graph and not just the latest instance. In this paper we have two goals: 1) provide a concise survey of the state-of-art with respect to systems in historical graph management since no such comprehensive discussion exists and 2) propose an architecture for a distributed historical graph management system (named MAGMA - MAssive Graph MAnagement) based on previous research work of the authors.
Alexandros Spitalas, Kostas Tsichlas
New Results in Priority-Based Bin Packing
Abstract
In this paper, we discuss new algorithmic results for bin minimization in the subset-constrained variant of Priority-based bin packing (PBBP-SC). This problem was introduced in [21], as an abstract model for capturing certain issues in database migration and palleting. This paper focuses on new fine-grained complexity results for the bin minimization problem (BMP) under two distinct parameterizations. We also provide a detailed empirical analysis of integer programming formulations for the problems discussed in this paper.
K. Subramani, P. Wojciechowski, Alvaro Velasquez
More Sparking Soundex-Based Privacy-Preserving Record Linkage
Abstract
Privacy preserving record linkage refers to the problem of matching records from two or more data holders without revealing any personal identifiers, thus, maintaining the privacy of the individuals described by these records. While parallel processing has been deployed in the context of privacy preserving record linkage for handling big data, in this paper, we further explore parallel methods based on Apache Spark and phonetic codes and propose improvements, which manage to achieve superior performance with respect to time efficiency and privacy characteristics. To support our claims, we provide extensive experimental results and a rigorous discussion.
Alexandros Karakasidis, Georgia Koloniari
Privacy Preserving Queries of Shortest Path Distances
Abstract
Consider a user with a very limited hardware and internet connection who wants to query a shortest path distance from a web service, but doesn’t want to reveal the source and destination to the server. Using state-of-the-art methods, we show that we can privately query shortest path distances in this case, if we are allowed to use three non-cooperating servers of moderate compute and communication power. We argue that this is not possible with classical shortest path algorithms. Finally, we give some experiments showing the feasibility of the approach.
Ernst Althaus, Stefan Funke, Moritz Schrauth
Backmatter
Metadata
Title
Algorithmic Aspects of Cloud Computing
Editors
Luca Foschini
Spyros Kontogiannis
Copyright Year
2023
Electronic ISBN
978-3-031-33437-5
Print ISBN
978-3-031-33436-8
DOI
https://doi.org/10.1007/978-3-031-33437-5