SQL database primitives for decision tree classifiers

Sattler, Kai-Uwe; Dunemann, Oliver

doi:10.1145/502585.502650

Cited by 39 publications

(14 citation statements)

References 18 publications

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“…A lot of research has been done in implementing data mining algorithms in database query languages. Previous work suggests that tight coupling of the data mining algorithms to the database systems improves the performance of the algorithms significantly [4]. We aim to implement α-miner algorithm in Structured Query Language (SQL) so that our Process Discovery application is tightly coupled to the database.…”

Section: Research Motivation and Aimmentioning

confidence: 99%

Pragamana

Gupta

Sachdev

Sureka³

2008

Proceedings of the Eighth International C* Conference on Computer Science &Amp; Software Engineering - C3S2E '15

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Process-Aware Information Systems (PAIS) is an IT system that support business processes and generate large amounts of event logs from the execution of business processes. An event log is represented as a tuple of CaseID, Timestamp, Activity and Actor. Process Mining is a new and emerging field that aims at analyzing the event logs to discover, enhance and improve business processes and check conformance between run time and design time business processes. The large volume of event logs generated are stored in the databases. Relational databases perform well for a certain class of applications. However, there are a certain class of applications for which relational databases are not able to scale. To handle such class of applications, NoSQL database systems emerged. Discovering a process model (workflow model) from event logs is one of the most challenging and important Process Mining task. The α-miner algorithm is one of the first and most widely used Process Discovery technique. Our objective is to investigate which of the databases (Relational or NoSQL) performs better for a Process Discovery application under Process Mining. We implement the α-miner algorithm on relational (row-oriented) and NoSQL (column-oriented) databases in database query languages so that our algorithm is tightly coupled to the database. We present a performance benchmarking and comparison of the α-miner algorithm on row-oriented database and NoSQL column-oriented database so that we can compare which database can efficiently store massive event logs and analyze it in seconds to discover a process model.

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Section: Research Motivation and Aimmentioning

confidence: 99%

Pragamana

Gupta

Sachdev

Sureka³

2008

Proceedings of the Eighth International C* Conference on Computer Science &Amp; Software Engineering - C3S2E '15

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“…There is a special approach on the same problem using set containment and relational division to find associations [18]. Database primitives to mine decision trees are proposed in [9,21]. Implementing a clustering algorithm in SQL is explored in [14].…”

Section: Related Workmentioning

confidence: 99%

Horizontal aggregations for building tabular data sets

Ordóñez

2004

Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

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In a data mining project, a significant portion of time is devoted to building a data set suitable for analysis. In a relational database environment, building such data set usually requires joining tables and aggregating columns with SQL queries. Existing SQL aggregations are limited since they return a single number per aggregated group, producing one row for each computed number. These aggregations help, but a significant effort is still required to build data sets suitable for data mining purposes, where a tabular format is generally required. This work proposes very simple, yet powerful, extensions to SQL aggregate functions to produce aggregations in tabular form, returning a set of numbers instead of one number per row. We call this new class of functions horizontal aggregations. Horizontal aggregations help building answer sets in tabular form (e.g. point-dimension, observation-variable, instancefeature), which is the standard form needed by most data mining algorithms. Two common data preparation tasks are explained, including transposition/aggregation and transforming categorical attributes into binary dimensions. We propose two strategies to evaluate horizontal aggregations using standard SQL. The first strategy is based only on relational operators and the second one uses the "case" construct. Experiments with large data sets study the proposed query optimization strategies.

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“…Dies sind Anfragen, die eine Bedingung der Form P 1 ∧ P 2 ∧ · · · ∧ P m enthalten, wobei P i ein Prädikat Hier kann mit einer speziellen Zugriffsstruktur, die PartialMatch-Anfragen auf mehrdimensionalen Daten besonders gut unterstützt, ein Vorteil gegenüber den herkömmlichen Indexstrukturen erzielt werden. In [41] wurde das multidimensionale Hashing (MDH) als eine solche Zugriffsstrukur identifiziert.…”

Section: Datenanalyseunclassified