Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe

The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.</p

A Scenario-View Based Approach to Analyze External Behavior of Web Services for Supporting Mediated Service Interactions

Web service interactions have triggered the initiative to identify and solve mismatches from a behavioral aspect. Current approaches are limited since they mainly focus on control-flow but largely ignore data-flow. In this paper, we propose an approach to automatically generate scenarios and views for describing external behavior of Web services, i.e. the public process, considering both control-flow and data-flow. We define a scenario as a set of complete execution paths for a public process. Data dependencies are presented as a dependency graph, which is optimized into a minimal dependency graph. Then, a view is generated to describe a scenario for analysis purposes, and external behavior of a Web service is described as a finite set of views. Our approach is very useful for service modelers and users to better understand the external behavior of Web services, to identify and solve mismatches from a behavioral aspect, and thus to facilitate Web service interactions.peer-reviewe

A Framework for Testing Algorithmic Trading Strategies

Algorithmic trading and artificial stock markets have generated huge interest not only among brokers and traders in the financial markets but also across various disciplines in the academia. The emergence of algorithmic trading has created a new environment where the classic way of trading requires new approaches. In order to understand the impact of such a trading process on the functioning of the market, new tools, theories and approaches need to be created. Thus artificial stock markets have emerged as simulation environments to test, understand and model the impact of algorithmic trading, where humans and software agents may compete on the same market. The purpose of this paper is to create a framework to test and analyse various trading strategies in a dedicated artificial environment

Process Mediation Based on Triple Space Computing

Web services are inherently heterogeneous at both data and behavioral levels because of the nature of the Web, which is the main obstacle to the usability of Web services. The heterogeneity at a behavioral level is generally addressed by process mediation, in which the message flow is adjusted to suit the behavior of Web services involved in a given interaction. In this paper, we present a novel approach for process mediation, and propose an architectural for process mediation based on Triple Space Computing to solve resolvable message sequence mismatches. These resolvable mismatches can be classified into five classes for unveiling their essence. This work provides a basis for the generalization of mismatches themselves, as well as a potentially uniform solution to address these mismatches.peer-reviewe

An XML to WSML Adapter Implementation

This paper describes an implementation of an Adapter that converts XML to a Web Service Modeling Language (WSML) [1]. WSML is the language used to describe Web Service Modeling Ontology (WSMO) [2] concepts, related to Semantic Web services (SWS). SWS are web services that are semantically annotated. The semantic annotation is necessary to address various business logics in an appropriate manner, thus allowing complex business applications to be built and executed. The Web Service Execution Environment (WSMX) [3] is an execution environment for dynamic discovery, selection, mediation and invocation of semantic web services. WSMX is a reference implementation for WSM

A Scenario-View Based Approach to Analyze External Behavior of Web Services for Supporting Mediated Service Interactions

Web service interactions have triggered the initiative to identify and solve mismatches from a behavioral aspect. Current approaches are limited since they mainly focus on control-flow but largely ignore data-flow. In this paper, we propose an approach to automatically generate scenarios and views for describing external behavior of Web services, i.e. the public process, considering both control-flow and data-flow. We define a scenario as a set of complete execution paths for a public process. Data dependencies are presented as a dependency graph, which is optimized into a minimal dependency graph. Then, a view is generated to describe a scenario for analysis purposes, and external behavior of a Web service is described as a finite set of views. Our approach is very useful for service modelers and users to better understand the external behavior of Web services, to identify and solve mismatches from a behavioral aspect, and thus to facilitate Web service interactions

Social sentiment indices powered by X-scores

Social Sentiment Indices powered by X-Scores (SSIX) seeks to address the challenge of extracting relevant and valuable economic signals in a cross-lingual fashion from the vast variety of and increasingly influential social media services, such as Twitter, Google+, Facebook, StockTwits and LinkedIn, and in conjunction with the most reliable and authoritative newswires, online newspapers, financial news networks, trade publications and blogs. A statistical framework of qualitative and quantitative parameters called X-Scores will power SSIX. This framework will interpret economically significant sentiment signals that are disseminated in the social ecosystem. Using X-Scores, SSIX will create commercially viable and exploitable social sentiment indices, regardless of language, locale and data format. SSIX and X-Scores will support research and investment decision making for European SMEs, enabling end users to analyse and leverage real-time social media sentiment data in their domain, creating innovative products and services to support revenue growth with focus on increased alpha generation for investment portfolios.European Union’s Horizon 2020 Research and Innovation Programme ICT 2014 - Information and Communications Technologies under grant agreement No. 645425.peer-reviewe

Social sentiment indices powered by X-scores

Social Sentiment Indices powered by X-Scores (SSIX) seeks to address the challenge of extracting relevant and valuable economic signals in a cross-lingual fashion from the vast variety of and increasingly influential social media services, such as Twitter, Google+, Facebook, StockTwits and LinkedIn, and in conjunction with the most reliable and authoritative newswires, online newspapers, financial news networks, trade publications and blogs. A statistical framework of qualitative and quantitative parameters called X-Scores will power SSIX. This framework will interpret economically significant sentiment signals that are disseminated in the social ecosystem. Using X-Scores, SSIX will create commercially viable and exploitable social sentiment indices, regardless of language, locale and data format. SSIX and X-Scores will support research and investment decision making for European SMEs, enabling end users to analyse and leverage real-time social media sentiment data in their domain, creating innovative products and services to support revenue growth with focus on increased alpha generation for investment portfolios.European Union’s Horizon 2020 Research and Innovation Programme ICT 2014 - Information and Communications Technologies under grant agreement No. 645425