Chive: A System for Collaborative Exploratory Data Analysis
نویسنده
چکیده
Exploratory data analysis is a process which involves rapid iteration and creation of multiple views of a dataset to explore the data, test hypotheses and find interesting patterns. To facilitate this process, we present Chive, a system for collaborative exploratory data analysis which keeps track of the complete history of analysts’ exploratory process, allows them to quickly revert to any visualisation produced in the process, to search through this history for related visualisations, and to collaborate by sharing their exploration histories with other analysts and building on their work.
منابع مشابه
A NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...
متن کاملCollaborative Visual Analysis of Sentiment in Twitter Events
Researchers across many fields are increasingly using data from social media sites to address questions about individual and group social behaviors. However, the size and complexity of these data sets challenge traditional research methods; many new tools and techniques have been developed to support research in this area. In this paper, we present our experience designing and evaluating Agave,...
متن کاملA Tele - Immersive Environment for Collaborative Exploratory Analysis of Massive Data Sets
This is a white paper outlining a methodology for employing collaborative, immersive virtual environments as a high-end visualization interface for massive data-sets.
متن کاملMusic Recommendations based on Implicit Feedback and Social Circles: The Last FM Data Set
The goal of recommender systems is to make personalized product recommendations based on users taste. In this paper we perform an exploratory analysis on the LastFM data set. Based on the data set properties we use collaborative filtering , latent factor models and propose community detection using clique percolation to give personalized artist recommendations to the user. We circumvent the imp...
متن کاملValidity & reliability of the Persian version of Grasha-Richmann student learning styles scale
Introduction: The present study aimed to investigate the psychometricproperties of Grasha-Riechmann Student Learning Styles Scale.Method: The participants included 1039 students (421 students in human and 618 students in technical sciences), selected through the stratified sampling method from Tehran University. They answered the Grasha-Riechmann student learning style scale and the data was an...
متن کامل