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1. Geochemical databases

   https://doi.org/10.1016/B978-0-323-99762-1.00123-6  

   Klöcking, Marthe; Lehnert, Kerstin A; Wyborn, Lesley (September 2024, Elsevier)

   Geochemistry is a data-driven discipline. Modern laboratories produce highly diverse data, and the recent exponential increase in data volumes is challenging established practices and capabilities for organizing, analyzing, preserving, and accessing these data. At the same time, sophisticated computational techniques, including machine learning, are increasingly applied to geochemical research questions, which require easy access to large volumes of high-quality, well-organized, and standardized data. Data management has been important since the beginning of geochemistry but has recently become a necessity for the discipline to thrive in the age of digitalization and artificial intelligence. This paper summarizes the landscape of geochemical databases, distinguishing different types of data systems based on their purpose, and their evolution in a historic context. We apply the life cycle model of geochemical data; explain the relevance of current standards, practices, and policies that determine the design of modern geochemical databases and data management; the ethics of data reuse such as data ownership, data attribution, and data citation; and finally create a vision for the future of geochemical databases: data being born digital, connected to agreed community standards, and contributing to global democratization of geochemical data. 

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2. Towards Multiverse Databases

   https://doi.org/10.1145/3317550.3321425  

   Marzoev, Alana; Araújo, Lara Timbó; Schwarzkopf, Malte; Yagati, Samyukta; Kohler, Eddie; Morris, Robert; Kaashoek, M. Frans; Madden, Sam (May 2019, HotOS '19: Proceedings of the Workshop on Hot Topics in Operating Systems)

   A multiverse database transparently presents each application user with a flexible, dynamic, and independent view of shared data. This transformed view of the entire database contains only information allowed by a centralized and easily-auditable privacy policy. By enforcing the privacy policy once, in the database, multiverse databases reduce programmer burden and eliminate many frontend bugs that expose sensitive data. Multiverse databases' per-user transformations risk expensive queries if applied dynamically on reads, or impractical storage requirements if the database proactively materializes policy-compliant views. We propose an efficient design based on a joint dataflow across "universes" that combines global, shared computation and cached state with individual, per-user processing and state. This design, which supports arbitrary SQL queries and complex policies, imposes no performance overhead on read queries. Our early prototype supports thousands of parallel universes on a single server. 

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3. Adaptive Schema Databases

   Spoth, W.; Arab, B. S.; Chan, E. S.; Gawlick, D.; Ghoneimy, A.; Glavic, B.; Hammerschmidt, B.; Kennedy, O.; Lee, S.; Liu, Z. H.; et al (January 2017, CIDR 2017, 8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA, January 8-11, 2017, Online Proceedings)

   The rigid schemas of classical relational databases help users in specifying queries and inform the storage organization of data. However, the advantages of schemas come at a high upfront cost through schema and ETL process design. In this work, we propose a new paradigm where the database system takes a more active role in schema development and data integration. We refer to this approach as adaptive schema databases (ASDs). An ASD ingests semi-structured or unstructured data directly using a pluggable combination of extraction and data integration techniques. Over time it discovers and adapts schemas for the ingested data using information provided by data integration and information extraction techniques, as well as from queries and user-feedback. In contrast to relational databases, ASDs maintain multiple schema workspaces that represent individualized views over the data, which are fine-tuned to the needs of a particular user or group of users. A novel aspect of ASDs is that probabilistic database techniques are used to encode ambiguity in automatically generated data extraction workflows and in generated schemas. ASDs can provide users with context-dependent feedback on the quality of a schema, both in terms of its ability to satisfy a user's queries, and the quality of the resulting answers. We outline our vision for ASDs, and present a proof of concept implementation as part of the Mimir probabilistic data curation system. 

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4. Probabilistic Databases for All

   https://doi.org/10.1145/3375395.3389129  

   Suciu, Dan (June 2020, PODS)

   In probabilistic databases the data is uncertain and is modeled by a probability distribution. The central problem in probabilistic databases is query evaluation, which requires performing not only traditional data processing such as joins, projections, unions, but also probabilistic inference in order to compute the probability of each item in the answer. At their core, probabilistic databases are a proposal to integrate logic with probability theory. This paper accompanies a talk given as part of the Gems of PODS series, and describes several results in probabilistic databases, explaining their significance in the broader context of model counting, probabilistic inference, and Statistical Relational Models. 

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5. Detecting Correlated Gaussian Databases

   Kahraman, Zeynep; Nazer, Bobak (July 2022, IEEE International Symposium on Information Theory)