Tootfinder

Automatic Recommendations for Evolving Relational Databases Schema
Anne Etien, Nicolas Anquetil
https://arxiv.org/abs/2404.08525 https://

Automatic Recommendations for Evolving Relational Databases Schema
Relational databases play a central role in many information systems. Their schema contains structural (e.g. tables and columns) and behavioral (e.g. stored procedures or views) entity descriptions. Then, just like for ``normal'' software, changes in legislation, offered functionalities, or functional contexts, impose to evolve databases and their schemas. But in some scenarios, it is not so easy to deconstruct a wished evolution of the schema into a precise sequence of operations. Changing a d…

This https://arxiv.org/abs/2404.07135 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards Robustness of Text-to-Visualization Translation against Lexical and Phrasal Variability
Text-to-Vis is an emerging task in the natural language processing (NLP) area that aims to automatically generate data visualizations from natural language questions (NLQs). Despite their progress, existing text-to-vis models often heavily rely on lexical matching between words in the questions and tokens in data schemas. This overreliance on lexical matching may lead to a diminished level of model robustness against input variations. In this study, we thoroughly examine the robustness of curre…

This https://arxiv.org/abs/2309.03685 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education…

ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou
https://arxiv.org/abs/2403.05132

ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction, have certain limitations. Extracting structured information from natural language that deviates from known schemas or instructions has proven challenging for previous prompt-based methods. This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured infor…

SLSM : An Efficient Strategy for Lazy Schema Migration on Shared-Nothing Databases
Zhilin Zeng, Hui Li, Xiyue Gao, Hui Zhang, Huiquan Zhang, Jiangtao Cui
https://arxiv.org/abs/2404.03929

SLSM : An Efficient Strategy for Lazy Schema Migration on Shared-Nothing Databases
By introducing intermediate states for metadata changes and ensuring that at most two versions of metadata exist in the cluster at the same time, shared-nothing databases are capable of making online, asynchronous schema changes. However, this method leads to delays in the deployment of new schemas since it requires waiting for massive data backfill. To shorten the service vacuum period before the new schema is available, this paper proposes a strategy named SLSM for zero-downtime schema migrat…

I know AI is rubbish etc but I really just couldnt with this Shelly website that publishes no swagger or schemas for their APIs annot only that but the html tables arent complete....so pasted all this rubbish into ChatGPT and this actually saved a bunch of time, obvious need to double check everything but at least a tad useful.
(note missing SSID and RSSI from the spec and then see the comments etc in the result, niiiice)

ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou
https://arxiv.org/abs/2403.05132

ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction, have certain limitations. Extracting structured information from natural language that deviates from known schemas or instructions has proven challenging for previous prompt-based methods. This motivated us to explore domain-specific modeling in chat-based language models as a solution for extracting structured infor…

SLSM : An Efficient Strategy for Lazy Schema Migration on Shared-Nothing Databases
Zhilin Zeng, Hui Li, Xiyue Gao, Hui Zhang, Huiquan Zhang, Jiangtao Cui
https://arxiv.org/abs/2404.03929

SLSM : An Efficient Strategy for Lazy Schema Migration on Shared-Nothing Databases
By introducing intermediate states for metadata changes and ensuring that at most two versions of metadata exist in the cluster at the same time, shared-nothing databases are capable of making online, asynchronous schema changes. However, this method leads to delays in the deployment of new schemas since it requires waiting for massive data backfill. To shorten the service vacuum period before the new schema is available, this paper proposes a strategy named SLSM for zero-downtime schema migrat…

This https://arxiv.org/abs/2311.09736 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

CARE: Extracting Experimental Findings From Clinical Literature
Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world complexity and nuance required. Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities a…

This https://arxiv.org/abs/2402.13284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Structure Guided Large Language Model for SQL Generation
Generating accurate Structured Querying Language (SQL) is a long-standing problem, especially in matching users' semantic queries with structured databases and then generating structured SQL. Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL. However, such solutions overlook the structural information within user queries and databases, which can be utilized to enhance the generation of…

Structure Guided Large Language Model for SQL Generation
Qinggang Zhang, Junnan Dong, Hao Chen, Wentao Li, Feiran Huang, Xiao Huang
https://arxiv.org/abs/2402.13284

Structure Guided Large Language Model for SQL Generation
Generating accurate Structured Querying Language (SQL) is a long-standing problem, especially in matching users' semantic queries with structured databases and then generating structured SQL. Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL. However, such solutions overlook the structural information within user queries and databases, which can be utilized to enhance the generation of…

This https://arxiv.org/abs/2308.03584 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Polystore Architecture Using Knowledge Graphs to Support Queries on Heterogeneous Data Stores
Modern applications commonly need to manage dataset types composed of heterogeneous data and schemas, making it difficult to access them in an integrated way. A single data store to manage heterogeneous data using a common data model is not effective in such a scenario, which results in the domain data being fragmented in the data stores that best fit their storage and access requirements (e.g., NoSQL, relational DBMS, or HDFS). Besides, organization workflows independently consume these fragme…