As I agreed on contributing a piece to this series of which Tamer is in charge, I read all the preceding articles to get inspiration. The impression I got was, "gosh, I wish I had known all this back in my mid-career days and even these days!" For example, I did place students on my collaborative projects, but would have made faster progress in some projects if I had insisted on having weekly meetings. Or I should have considered a more detailed list of factors before committing to a task of considerable amount of work.

Attribution scores can be applied in data management to quantify the contribution of individual items to conclusions from the data, as part of the explanation of what led to these conclusions. In Artificial Intelligence, Machine Learning, and Data Management, some of the common scores are deployments of the Shapley value, a formula for profit sharing in cooperative game theory. Since its invention in the 1950s, the Shapley value has been used for contribution measurement in many fields, from economics to law, with its latest researched applications in modern machine learning. Recent studies investigated the application of the Shapley value to database management. This article gives an overview of recent results on the computational complexity of the Shapley value for measuring the contribution of tuples to query answers and to the extent of inconsistency with respect to integrity constraints. More specifically, the article highlights lower and upper bounds on the complexity of calculating the Shapley value, either exactly or approximately, as well as solutions for realizing the calculation in practice.

Cross-Trust-Domain Processing. Data is now a commodity. We know how to compute and store it efficiently and reliably at scale. We have, however, paid less attention to the notion of trust. Yet, data owners today are no longer the entities storing or processing their data (medical records are stored on the cloud, data is shared across banks, etc.). In fact, distributed systems today consist of many different parties, whether it is cloud providers, jurisdictions, organisations or humans. Modern data processing and storage always straddles trust domains.

We report our experience in running three editions (2020, 2021, 2022) of the SIGMOD programming contest, a well-known event for students to engage in solving exciting data management problems. During this period we had the opportunity of introducing participants to the entity resolution task, which is of paramount importance in the data integration community. We aim at sharing the executive decisions, made by the people coauthoring this report, and the lessons learned.

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.

This issue's contributors highlight their influences when it comes to their research agenda on parallel data processing and skyline queries, respectively. Enjoy reading! While I will keep inviting members of the data management community, and neighboring communities, to contribute to this column, I also welcome unsolicited contributions. Please contact me if you are interested.

The workshop took place in Zurich and online from August 2 to 4, 2022. It was attended by researchers from 17 academic institutions and industry labs, including Microsoft Gray Systems Lab, Omics Data Automation, Oracle Labs Zurich, RelationalAI, and TigerGraph. It featured 18 talks and plenty of opportunities for discussions. The vast majority of participants attended in person.

The Diversity, Equity and Inclusion (DEI) initiative started as the Diversity/Inclusion initiative in 2020 [4]. The current report summarizes our activities in 2022. Our responsibility as a community is to ensure that attendees of DB conferences feel included, irrespective of their scientific perspective and personal background. One of the first steps was to establish the role of the DEI chairs at DB Conferences, with the DEI team dedicated to providing leadership to help our community achieve this goal. In this leadership role, the DEI team is advising DEI chairs at DB conferences, serving as a memory of DEI events at conferences, building an agreed-upon vision, and committing to working together to devise a set of measures for achieving DEI. That is pursued via actions led by our core members (Figure 1) and liaisons of individual executive bodies (Figure 2): REACH OUT collects data and experiences from our community. INCLUDE monitors and recommends inclusion efforts. ORGANIZE focuses on in-conference organization efforts, such as adopting a code of conduct. INFORM communicates through various channels. SUPPORT coordinates DEI support from executive bodies and sponsors. SCOUT collates DEI efforts from other communities. COORDINATE manages all actions. Two new actions: MEDIA preserves and disseminates the digital media produced by DEI@DB events. ETHICS establishes and promotes ethics guidelines for publications in our community.

"Data Scientist" has often been considered as the sexiest job of the 21st century. As a consequence, the spectrum of data science education programs has increased significantly in recent years, and there is a high demand for data scientists at many companies. However, what training is required to become a data scientist? What is the role of data scientists in current enterprises? Is the training well-aligned to the practical needs of a job? In this article, we will address these questions by evaluating a survey of people who were trained in a continuing education program in data science in Switzerland. Our study sheds lights on the practical aspects of the data science education and how this newly-gained knowledge can successfully be applied in an enterprise. One of the highlights from the point of view of the database community is the important role of SQL in data science.

Conjunctive queries with predicates in the form of comparisons that span multiple relations have regained interest recently, due to their relevance in OLAP queries, spatiotemporal databases, and machine learning over relational data. The standard technique, predicate pushdown, has limited efficacy on such comparisons. A technique by Willard can be used to process short comparisons that are adjacent in the join tree in time linear in the input size plus output size. In this paper, we describe a new algorithm for evaluating conjunctive queries with both short and long comparisons, and identify an acyclic condition under which linear time can be achieved. We have also implemented the new algorithm on top of Spark, and our experimental results demonstrate order-of-magnitude speedups over SparkSQL on a variety of graph patterns and analytical queries.