Titlebook: Cooperative Information Systems; 28th International C Mohamed Sellami,Paolo Ceravolo,Hervé Panetto Conference proceedings 2022 The Editor(s

蚀刻术

Moderate

,Why So Many Prices End with “9”,ing management. In this system, we define some rules through smart contracts to implement different business logic. Using the characteristics of decentralization and high credibility of the blockchain, it solves the problems of uneven training quality and untraceable training records in the traditio

定点

Relevance-Based Big Data Exploration for Smart Road Maintenancee proposed approach is grounded on three components: (i) a multi-dimensional model, apt to represent the road network and to enable data exploration; (ii) data summarisation techniques, in order to simplify overall view over high volumes of data collected by vehicles; (iii) a measure of relevance, a

CERE

Quantifying Temporal Privacy Leakage in Continuous Event Data Publishingsult in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.

使激动

avulsion

Enabling Multi-process Discovery on Graph Databasesent data, we address the question of . We propose in-database process discovery operators based on labeled property graphs. We use Neo4j as a DBMS and Cypher as a query language. We compare different discovery strategies that involve graph and relational databases. Our results show that process disc

maudtin

Collaborative Patterns for Workflows with Collaborative Robotsfor one to many and many to many relations between robots and humans in workflows. First, we formalize the collaboration pattern and its types (synchronized, cooperation, collaboration and coexistence). Our approach allows for the specification of time-based, spatial and functional constraints at ta

initiate

: Partitioned ,-Trees to Enable Compact and Dynamic Graphs in , overhead and the inability to represent the graph entirely in main memory, or ii) in a distributed architecture, where the graph dataset is partitioned over a cluster of machines with each one storing in main memory only a fragment (shard) of the (uncompressed) graph. We present ., our proposal whic

Aspiration

A Distributed Architecture for Privacy-Preserving Optimization Using Genetic Algorithms and Multi-paandidate solutions to the assignment problem while a Privacy Engine component evaluates the population in each iteration step. The participants’ private inputs are kept from competitors and not even the platform provider knows those inputs, receiving only encrypted input which is processed by MPC no

Mammal