Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Metanome - Data Profiling

Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies.

The Metanome project is a project at HPI in cooperation with the Qatar Computing Reserach Institute (QCRI). Metanome provides a fresh view on data profiling by developing and integrating efficient algorithms into a common tool, expanding on the functionality of data profiling, and addressing performance and scalabilities issues for Big Data. A vision of the Metanome project appeared in SIGMOD Record "Data Profiling Revisited" and demo of the Metanome profiling tool was given at VLDB 2015 "Data Profiling with Metanome".

Algorithm Research

Active:

Past:

Tool Development

Active:

  • Tanja Bergmann (Backend, Frontend, and Architecture)
  • Vincent Schwarzer (Backend and Architecture)
  • Maxi Fischer (Backend and Frontend)

Past:

  • Moritz Finke (Backend and Architecture)
  • Carl Ambroselli (Frontend)
  • Jakob Zwiener (Backend and Architecture)
  • Claudia Exeler (Frontend)

Projects within Metanome

  • Unique column combination discovery
    As prerequisite for unique constraints and keys, UCCs are a basic piece of metadata for any table. The problem is of particular complexity when regarding the exponential number of column combinations. We adress the problem by parallelization and pruning strategies.
    This work is in collaboration with QCRI
  • Inclusion dependency discovery
    As prerequisite of foreign keys, INDs can tell us how tables within a schema can be connected. When regarding tables of different data sources, conditional IND discovery is of particular relevance.
    See also the completed Aladin project and publications by Jana Bauckmann et al., in particular our Spider algorithm.
  • Incremental dependency discovery
    We are extending our work on UCC and IND discovery to tables that receive incremental updates. The goal is to avoid a complete re-computation and restrict processing to relevant columns, records, and dependencies.
  • Profiling and Mining RDF data
    The <subject, predicate, object> data model of RDF necessitates new approaches to basic profiling and data mining methods. 
    See also: ProLOD++ demo
  • Functional dependency discovery
    Functional dependencies express relationships between attributes of a database relation and are extensively used in data analysis and database design, especially schema normalization. We contribute to research in this area by evaluating current state-of-the-art algorithms and developing faster and more scalable approaches.
    See also: FD algorithms
  • Order dependency discovery
    Order dependencies (ODs) describe a relationship of order between lists of attributes in a relational table. ODs can help to understand the semantics of datasets and the appli- cations producing them. The existence of an OD in a table can provide hints on which integrity constraints are valid for the domain of the data at hand. Moreover, order dependen- cies have applications in the field of query optimization by suggesting query rewrites.
    See also: OD algorithms

Teaching Data Profiling

Student projects

  • Master's project "Profiling Dynamic Data" (4 students, winter 16/17)
  • Master's project "Approximate Data Profiling" (10 students, summer 2015)
  • Master's project "Metadata Trawling" (4 students, winter 14/15)
  • Master's project "Joint Data Profiling" (4 students, winter 13/14)
  • Master's project "Piggy-back Profiling" (6 students, winter 13/14)
  • Bachelor's project "ProCSIA: Profiling column stores with IBM's Information Analyzer" (8 students, summer 2011)

Current and past master theses

  • Please see these links for ongoing and completed master's theses, many of which are in the data profiling area. All theses are available as pdf - just contact Felix Naumann.

Courses

Publications

Data Profiling with Metanome (demo)

Thorsten Papenbrock, Tanja Bergmann, Moritz Finke, Jakob Zwiener, Felix Naumann
Proceedings of the VLDB Endowment, vol. 8(12):1860-1871 2015

Abstract:

Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies. We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic discovery of complex metadata. Metanome’s goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the at times large metadata sets.

Keywords:

metanome,profiling,hpi

BibTeX file

@article{papenbrock2015metanome,
author = { Thorsten Papenbrock, Tanja Bergmann, Moritz Finke, Jakob Zwiener, Felix Naumann },
title = { Data Profiling with Metanome (demo) },
journal = { Proceedings of the VLDB Endowment },
year = { 2015 },
volume = { 8 },
number = { 12 },
pages = { 1860-1871 },
month = { 0 },
abstract = { Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies. We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic discovery of complex metadata. Metanome’s goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the at times large metadata sets. },
keywords = { metanome,profiling,hpi },
publisher = { VLDB Endowment },
booktitle = { Proceedings of the International Conference on Very Large Data Bases (PVLDB) },
issn = { 2150-8097 },
priority = { 0 }
}

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last change: Tue, 12 Apr 2016 15:38:29 +0200