World's Best Scientists 2026 revealed!
Dagstuhl Publishing

International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP)

Location: Edinburgh , United Kingdom

Conference dates: 3/29/2022 - 3/29/2022

Research H-index
3

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 890 8 9 3

Call for Papers

Long papers include novel and mature research, industrial or survey work. Long papers of good quality but not mature enough might be accepted to the workshop as short papers. Short papers include:

On-going: novel research works with preliminary results.

Vision: position papers outlining research issues for future work.

DOLAP includes a second review round for uncertain cases where either there is no agreement among the reviewers, or they ask for a clarification prior to accepting a paper.

Special Theme: Responsible Data Science. Data Science promises to bring significant improvements in people’s lives, accelerating knowledge discovery and innovation. However, lately, there has been an increasing concern regarding the lack of diversity (leading to exclusion), fairness (leading to discrimination), and transparency (leading to opacity) when making critical decisions. This motivates the need for methods, tools, and systems to ensure that data are used responsibly, especially in applications such as healthcare, education, and public policy. To promote novel solutions to this urgent problem, DOLAP 2022 will devote a special session to Responsible Data Science. Relevant topics include, but are not limited to:

Explainable and interpretable analytics

Bias in big data and how to mitigate it

Data quality and data cleaning

FAIRness (Findability, Accessibility, Interoperability and Reusability) in OLAP

Additional research topics include, but are not limited to:

Design and Language

Data management fundamentals: architectures, design, ETL/ELT, modeling, data integration, database design for big data, query processing, maintenance, evolution, security, personalization and privacy in decision support systems.

Data Variety: unstructured data (e.g., text), semi-structured data (e.g., XML), multimedia, spatial, temporal, and spatio-temporal data, stream and sensor data, semantic Web & deep Web, data lakes, data spaces, data quality, graph data.

Optimization

Coping with Volume: physical organization, performance optimization and tuning, scalability, MapReduce and Spark for data analytics, performance optimization of ETL/ELT.

Coping with Velocity: Deployment on parallel machine, database clusters, cloud infrastructures, smart grid, active/real-time analytics, real-time queries.

Analytical Processing and Applications

Analytics and Value: OLAP, data exploration through visualization, recommendation, reformulation, approximate query-answering, personalization, result presentation, data storytelling, graph analytics, process mining, advanced visualization for business contexts.

Analytics and Veracity: heterogeneous data integration for analytics, quality aspects of data analysis, exploration outcome and end-user experience, fairness of data analysis, analytics and data driven decision making for the data enthusiasts.

Integration of analytics with machine learning, data mining, information retrieval, search engines, predictive and prescriptive analytics.

Big Data applications: smart city, smart health, smart energy.

Overview

The ranking presented on this page provides a comprehensive evaluation of scientific conferences in the field of Computer Science. This ranking has been meticulously prepared by Research.com, a recognized leader in the provision of scientific research data across all major research areas, including Computer Science. Since 2014, Research.com has been renowned for supplying trusted information on scientific contributions, supporting academics and practitioners worldwide.

Each conference’s position in the ranking is determined by a unique bibliometric score, developed exclusively by Research.com. This score is calculated through the estimated h-index and incorporates the number of leading scientists who have contributed to the conference within the last three years. This methodology ensures a thorough and balanced assessment of each conference's scholarly impact.

The current ranking includes Impact Score values collected as of 2024-11-27, guaranteeing the most up-to-date reflection of recent scientific activities. In creating this ranking, more than 2,742 conferences were carefully analyzed, following an exhaustive review process. This involved the rigorous examination of over 148,739 scientific documents published during the last three years, authored by 13,184 leading and widely respected scientists specializing in Computer Science.

The depth of this analysis, combined with the expertise of the professionals involved, ensures that the resulting ranking offers an authoritative and credible resource for the evaluation of scientific conferences. For those interested in a detailed explanation of the methodology used to compute these ranking scores, please visit our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing at Data Warehousing and OLAP (based on the number of publications) are:

  • Carlos Ordonez (19 papers) published 2 papers at the last edition, 1 more than at the previous edition,
  • Alfredo Cuzzocrea (12 papers) absent at the last edition,
  • Patrick Marcel (9 papers) published 4 papers at the last edition,
  • Stefano Rizzi (9 papers) published 2 papers at the last edition,
  • Alberto Abelló (8 papers) absent at the last edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing at Data Warehousing and OLAP (based on the number of publications) are:

  • University of Houston (19 papers) published 2 papers at the last edition, 1 more than at the previous edition,
  • University of Calabria (10 papers) absent at the last edition,
  • Polytechnic University of Catalonia (10 papers) absent at the last edition,
  • François Rabelais University (9 papers) published 4 papers at the last edition,
  • University of Bologna (9 papers) published 2 papers at the last edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2019 edition, 15.38% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 72.73% were posted by at least one author from the top 10 institutions publishing at the conference. Another 18.18% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 9.09% of all publications and 0.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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