Abstract
In this article, we present solutions to visualize and interact with linked data in historical archives considering three different scenarios: search, individual record view, and creation of relationships. The created solutions were designed using nonfunctional mockups and were based on the CIDOC-CRM model, a model created and applied in the museums community liable to be extended to other cultural heritage institutions, being our solutions an application of this model to archives. A sample of 20 archival professionals was selected to evaluate the elements included in the proposed solutions. The evaluation sessions consisted in structured interviews supported by an introductory video and a survey. The think-aloud protocol was applied during the sessions. We conducted both a quantitative and qualitative analysis to the collected answers. From this analysis, we conclude that the majority of the participants showed great receptivity to the solutions presented and recognized many benefits in the application of linked data. Our contributions also include an exploratory study of some existing linked data systems, giving particular attention to their visualization and interaction modes.

Abstract
In this paper we describe the EPISA Platform, a technical infrastructure designed and developed to support archival records management and access using linked data technologies. The EPISA Platform follows a client-server paradigm, with a central component, the EPISA Server, responsible for storage, reasoning, authorization, and search; and a frontend component, the EPISA ArchClient, responsible for user interaction. The EPISA Server uses Apache Jena Fuseki for storage and reasoning, and Apache Solr for search. The EPISA ArchClient is a web application implemented using PHP Laravel and standard web technologies. The platform follows a modular architecture, based on Docker containers. We describe the technical details of the platform and the main user interaction workflows, highlighting the abstractions developed to integrate linked data in the archival management process. The EPISA Platform has been successfully used to support research and development of linked data use in the archival domain in the context of the EPISA project.

Abstract
In this work, we targeted the search engine of a sports-related website that presented an opportunity for search result quality improvement. We reframed the engine as a Federated Search instance, where each collection represented a searchable entity type within the system, using Apache Solr for querying each resource and a Python Flask server to merge results. We extend previous work on individual search term weighing, making use of past search terms as a relevance indicator for user selected documents. To incorporate term weights we define four strategies combining two binary variables: integration with default relevance (linear scaling or linear combination) and search term frequency (raw value or log-smoothed). To evaluate our solution, we extracted two query sets from search logs: one with frequently submitted queries, and another with ambiguous result access patterns. We used click-through information as a relevance proxy and tried to mitigate its limitations by evaluating under distinct IR metrics, including MRR, MAP and NDCG. Moreover, we also measured Spearman rank correlation coefficients to test similarities between produced rankings and reference orderings according to user access patterns. Results show consistency across all metrics in both sets. Previous search terms were key to obtaining a higher effectiveness, with runs that used pure search term frequency performing best. Compared to the baseline, our best strategies were able to maintain quality on frequent queries and improve retrieval effectiveness on ambiguous queries, with up to six percentage points better performance on most metrics.

Abstract
Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces (APIs), compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

Abstract

Abstract
The internet is used by the majority of the world's population. Many of its contents are free for consumers, supported by digital marketing investment. The current large online population can render digital marketing campaigns inefficient for brands buying ads if the right message is not reaching the right audience. Customer Data Platforms (CDPs) enables brands to collect first-party data about their customers and leverage it to reach the right audience, without sharing private customer data with third parties. This paper documents how CDPs work, by detailing their main components as patterns and relating them as a pattern language. The language is composed of five patterns: Event Tracking, ID Matching, User Profile Storage, Segmentation, and Activation. The pattern language can be used by marketeers and engineers in digital marketing as an introduction or reference to how CDPs are designed and work. © 2022 ACM.