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1. A Step-Based Tutoring System to Teach Underachieving Students How to Construct Algebraic Models

   https://doi.org/10.1007/s40593-023-00328-3  

   VanLehn, Kurt; Milner, Fabio; Banerjee, Chandrani; Wetzel, Jon (January 2023, International Journal of Artificial Intelligence in Education)

   An algebraic model uses a set of algebraic equations to describe a situation. Constructing such models is a fundamental skill, but many students still lack the skill, even after taking several algebra courses in high school and college. For such students, we developed instruction that taught students to decompose the to-be-modelled situation into schema applications, where a schema represents a simple relationship such as distance-rate-time or part-whole. However, when a model consists of multiple schema applications, it needs some connection among them, usually representedby letting the same variable appear in the slots of two or more schemas. Students in our studies seemed to have more trouble identifying connections among schema applications than identifying the schema applications themselves. We developed several tutoring systems and evaluated them in university classes. One of them, a step-based tutoring system called OMRaaT (One Mathematical Relationship at a Time), was both reliably superior (p = 0.02, d = 0.67) to baseline and markedly superior (p < 0.001, d = 0.84) to an answer-based tutoring system using only commercially available software (MATLAB Grader). 

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2. Mining Logical Event Schemas From Pre-Trained Language Models

   https://doi.org/10.18653/v1/2022.acl-srw.25  

   Lawley, Lane; Schubert, Lenhart (May 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop)

   We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process. In lieu of a pre-made corpus of stories, our dataset is a continuous feed of “situation samples” from a pre-trained language model, which are then parsed into FrameNet frames, mapped into simple behavioral schemas, and combined and generalized into complex, hierarchical schemas for a variety of everyday scenarios. We show that careful sampling from the language model can help emphasize stereotypical properties of situations and de-emphasize irrelevant details, and that the resulting schemas specify situations more comprehensively than those learned by other systems. 

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3. Scooter & Sidecar: A Domain-Specific Approach to Writing Secure Database Migrations

   https://doi.org/10.1145/3453483.3454072  

   Renner, John; Sanchez-Stern, Alex; Brown, Fraser; Lerner, Sorin; Stefan, Deian (June 2021, Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation)

   null (Ed.)

   Web applications often handle large amounts of sensitive user data. Modern secure web frameworks protect this data by (1) using declarative languages to specify security policies alongside database schemas and (2) automatically enforcing these policies at runtime. Unfortunately, these frameworks do not handle the very common situation in which the schemas or the policies need to evolve over time—and updates to schemas and policies need to be performed in a carefully coordinated way. Mistakes during schema or policy migrations can unintentionally leak sensitive data or introduce privilege escalation bugs. In this work, we present a domain-specific language (Scooter) for expressing schema and policy migrations, and an associated SMT-based verifier (Sidecar) which ensures that migrations are secure as the application evolves. We describe the design of Scooter and Sidecar and show that our framework can be used to express realistic schemas, policies, and migrations, without giving up on runtime or verification performance. 

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4. 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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5. SchemaDrill: Interactive Semi-Structured Schema Design

   https://doi.org/10.1145/3209900.3209908  

   Spoth, William; Xie, Ting; Kennedy, Oliver; Yang, Ying; Hammerschmidt, Beda; Liu, Zhen Hua; Gawlick, Dieter (January 2018, Proceedings of the Workshop on Human-In-the-Loop Data Analytics)

   Ad-hoc data models like JSON make it easy to evolve schemas and to multiplex different data-types into a single stream. This flexibility makes JSON great for generating data, but also makes it much harder to query, ingest into a database, and index. In this paper, we explore the first step of JSON data loading: schema design. Specifically, we consider the challenge of designing schemas for existing JSON datasets as an interactive problem. We present SchemaDrill, a roll-up/drill-down style interface for exploring collections of JSON records. SchemaDrill helps users to visualize the collection, identify relevant fragments, and map it down into one or more flat, relational schemas. We describe and evaluate two key components of SchemaDrill: (1) A summary schema representation that significantly reduces the complexity of JSON schemas without a meaningful reduction in information content, and (2) A collection of schema visualizations that help users to qualitatively survey variability amongst different schemas in the collection. 

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