Arthur Howell’s Post

I’m thrilled to announce the launch of my new blog series focused on Data Structures and Algorithms (DSA)! 🚀 ⭐In this blog post the focus is on the Complexities in Data Structure Algorithms! ⭐ #DSA #COMPLEXITIES #BLOG

🔬 Empirical Analysis: Optimizing LLM Accuracy in Enterprise Data Querying Juan Sequeda presents quantitative research on LLM performance optimization: Key findings: - Baseline: GPT-4 zero-shot SQL → 16% accuracy - KG transformation layer → 54% accuracy - OBQC (Ontology-based Query Check) → 72% accuracy + 8% unknown states Technical implementation: - Built enterprise SQL schema (insurance domain) - Implemented semantic layer with ontology mappings - Developed SPARQL query validation against ontological constraints - Created error detection & repair pipeline using LLM feedback loops Significant insight: Incorrect path traversal in LLM-generated SPARQL queries was the primary failure mode, solved through ontological validation. Full technical deep-dive: https://lnkd.in/gUzZkRqh #KnowledgeGraphs #SPARQL #LLMs #DataEngineering #SemanticWeb

In a patient, step-by-step guide, Ashish Abraham explains how to leverage open-source framework Indexify to streamline property data management and to conduct advanced extraction and retrieval.

Successfully Completed Qualitative Data Analysis with MAXQDA Software at University of Hargeisa.

I am pleased to announce the publication of a new blog post that introduces FQL, a key-value (KV) query language designed to enhance data interaction and retrieval. This comprehensive overview explores the features and benefits of FQL, showcasing its potential to streamline queries and improve efficiency in data management. For those interested in expanding their knowledge in data querying languages, I invite you to read the full post and discover how FQL can transform the way you work with key-value data. Read more here: https://ift.tt/hqQNvdc

SPL provides professional syntax for structured data processing, which can describe complex calculations with concise code, has strong debugging interactivity, is easy to write big data and high-performance calculations, provides efficient file storage, excellent openness, and enterprise attributes, saves time in multiple dimensions, saves the lives of data scientists. 😄 😄

The Benchmark framework provides support for gathering performance measurements and statistics for operations on tabular data. It works from popular IDEs or from the command line. It is geared towards scale testing interfaces capable of ingesting table data, transforming it, and returning tabular results. Try it: https://hubs.li/Q02V8fdH0 #Deephaven #LiveDataframes #Benchmarks

Doing simple aggregations is dead simple using our AQL (analytical query language). Goodbye complex interfaces, goodbye convoluted queries. In this 2-min video, our PM, Thomas To, showed you how easy it is, to perform basic aggregations with AQL - whether it’s counting values within a model or calculating averages.

𝐒𝐓𝐀𝐓𝐈𝐒𝐓𝐈𝐂𝐀𝐋 𝐀𝐍𝐀𝐋𝐘𝐒𝐈𝐒 𝐎𝐅 𝐀𝐆𝐑𝐈𝐂𝐔𝐋𝐓𝐔𝐑𝐀𝐋 𝐑𝐄𝐒𝐄𝐀𝐑𝐂𝐇 𝐓𝐑𝐈𝐀𝐋𝐒 The ARM statistics engine and recommendations have evolved significantly in the past couple of years! Now featuring automatic check of ANOVA assumptions and not only recommends transformations, but runs those recommendations to determine if the transformation corrects the failed assumption. If the assumptions can't be met they now have non-parametric tools to rank the entries like in breeding trials. Also, there are now some spatial correction options including linear spatial trend, quadratic spatial trend and some nearest neighbor configurations. And now they have autoexports to R and SAS for data scientists wanting to get "under the hood" with the data and stats. I did run the webinar at 2x speed and still picked up a lot of interesting information and learned a thing or 2 about statistics too! @thierry doclot @sirieu data management @bernd stratmann @melissa welsh @matt elsinger @peter claussen @margaret kappenman @dawn waterman @michelle Nelson #r https://lnkd.in/e5xTuAYG

How to Use dataframe.map() for Element-wise Operations in Pandas https://ift.tt/Fg5HkqV Element-wise operations are a crucial part of data preprocessing in Pandas. Learn how to perform them with practical examples using the DataFrame.map() function. via KDnuggets https://ift.tt/3Wp1H2Z January 08, 2025 at 01:00PM