Data-driven evolutionary optimization has witnessed great success in solving complex real-world problems. However, existing data-driven algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage security threats if the must collected from different devices. To address above issue, this paper proposes a federated framework able p...

This volume collects the contributions presented at conference “Data-driven Decision Making” organized by Italian Association for Applied Statistics, held in Genoa from 12 to 14 September 2022. The papers cover a broad range of topics, with common thread: use statistical methods support decision-making both public administrations and private companies.

Specialized accelerators are becoming a standard way to achieve both high-performance and efficient computation. We see this trend extending all areas of computing, from low-power edge-computing systems processors in datacenters. Reconfigurable architectures, such as Coarse-Grained Arrays (CGRAs), attempt find balance between performance energy efficiency by trading off dynamism, flexibility, p...

Abstract Constructing modular applications from existing parts is difficult if there are mismatches due to input or output semantic differences during module interconnection. In order to minimize the effort of building such applications, and also as a guideline for designing modular applications from scratch, we propose an architecture in which modules are able to interface with each other with...

The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests. The approach is flexible and widely applicable, and robust optimization problems built from our new sets are computation...

The bottleneck of a data warehouse implementation is the ETL (extraction, transformation, and load) process, which carries out the initial population of the data warehouse and its further (usually periodical) updates. There is a number of software products supporting the OLAP analysis. However, the ETL process implementation is not repeatable in a significant way. This paper reports on a resear...

Deep Thought is a logic tutor where students practice constructing deductive logic proofs. Within Deep Thought is a data-driven mastery learning system (DDML), which calculates student proficiency based on rule scores weighted by expert-decided weights in order to assign problem sets of appropriate difficulty. In this study, we designed and tested a data-driven proficiency profiler (DDPP) metho...

CLIPS cannot effectively perform sound and complete logical inference in most real-world contexts. The problem facing CLIPS is its lack of goal generation. Without automatic goal generation and maintenance, Forward chaining can only deduce all instances of a relationship. Backward chaining, which requires goal generation, allows deduction of only that subset of what is logically true which is a...

Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic...