jeevan-mtech-thesis-presentation.pdf

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VISHLESHAN: Performance Comparison and Programming of Process Mining Algorithms in Graph-Oriented and Relational

Database Query Languages .

Jeevan Joishi [[email protected]]

MTech Research Associate, Software Analytics Research Lab (SARL)www.software-analytics.in

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MTech Thesis Evaluation Committee Members

Thesis AdviserProf. Ashish SurekaAdjunct Faculty at IIIT-Delhi and currently Visiting Researcher at Siemens Corporate Research and TechnologyFaculty In-charge, Software Analytics Research Lab (SARL)

External Examiner

Dr. Radha Krishna PisipatiPrincipal Research Scientist at Infosys Technologies Limited.

Internal Examiner

Prof. Sandip AineFaculty Member at IIIT-Delhi

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1. Research Motivation and Aim

2. Related Work and Novel Research Contributions

3. Implementation of Similar-Task and Sub-Contract Algorithm in SQL, RDBMS

4. Implementation of Similar-Task and Sub-Contract Algorithm in CYPHER, Graph Oriented

5. Experimental Dataset

6. Performance Comparison

7. Conclusion

8. Limitations

9. References

Outline

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3. Implementation of Alpha Algorithm in SQL, RDBMS

4. Implementation of Alpha Algorithm in CQL, Column Oriented



7. Conclusion

8. Limitations

9. References

Presentation Outline

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Research Motivation and Aim

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Why NoSQL?

Global population accessing internet has increasedtremendously.

Most applications are hosted on the cloud and need tosupport users 24 hours a day, 365 days a year.

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Introduction to NoSQL

Figure taken from [17]

Fig 1: Scale of internet usage.

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Why NoSQL?

Data is captured in huge volumes and consists of bothstructured and unstructured data.

Amount of data is growing rapidly and nature of data isgrowing as well.

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Fig 2: Growth of data.

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Why NoSQL?

What is wrong with relational databases?

Nothing!

Relational Databases employ one size fits all philosophyfor storage.

Relational Databases are used when strong consistency is amust.

Relational Databases can create problem when its time toscale.

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Why NoSQL?

Explosion of social media sites like Facebook, Twitter with large dataneeds.

They had to capture and deal with very large volumes of data in a waywhich was difficult to deal with traditional RDBMS.

Traditional databases are designed to scale up. We required a databasethat can scale out.

When relational applications become successful, usage goes up. Joinsare inherent in RDBMS and become very slow!

Application developers find it difficult to get the dynamic scalabilitythey need while maintaining the performance users demand.

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Why NoSQL?

We require a technology that scales out rather than scaling up!

Scale Up- Add more processor, memory.

Scale Out- Add more servers.

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Fig 3: Scale-up vs. Scale-out.

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NoSQL Database.

Hence, NoSQL databases were introduced: Not Only SQL

Non-relational data stores.

Do not require a fixed table schema.

Do not strictly follow on ACID properties of database,instead focus on CAP(Consistency, Availability, PartitionTolerance).

Column stores, Graph databases, Document stores.

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Introduction To NoSQL

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RDBMS vs. NoSQL

Scale up vs. Scale out

Normalization vs. De-normalization

ACID vs. CAP

Schema vs. Schema-less

Structured Data vs. Unstructured Data.

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Row Oriented vs. Graph Oriented

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Row Oriented vs. Graph Oriented Database


Record No

Name Address City State

01 Jeevan Joishi Uniworld Apartment Bangalore Karnataka

02 Kunal Gupta 15th Cross Road Kanpur Uttar Pradesh

03 Priyanka Verma Sector-7 Jind Haryana

04 Nidhi Agarwal JJ colony Bhiwani Haryana

Table 1: A RDBMS table.Fig 4: A Graph model.

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In row oriented, to read specific attributes, whole recordneeds to be read.

Joins in relational databases are compute-intensive tasks.

However, graph databases can read individual values basedon nodes, relationships or properties.

Graph databases avoid joins by traversing relationship(s)using index-free adjacency.

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Fig. 5: Relationships in Relational databases.

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Fig. 5: Relationships in Relational databases.

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Fig. 6: Relationships in Graph databases.

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Native Graph Processing using index-free adjacencyNon-Native Graph Processing using Global lookup indexFig 7: Fig 8:

Non-native vs. Native Graph Processing

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Process Mining

Process Mining is analysing a process using event log data.

One of the key aspects is to study the social structure of theorganization using event logs.

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Process Mining

Fig 9: Types of Process Mining Techniques

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Process Mining

Process Mining focuses on the analysis of process usingthe data present in event logs.

Each event in an event log record details in an activity.

Each event is associated with Case Identifiers (CaseID).

Each event has a timestamp.

Each event has an activity that is being performed.

An event has an actor that handles the event.

Additionally, each such event may include a unique identifier.

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Process Mining

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Process Mining

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Process Mining

Fig. 10: An example Event Log.

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Process Mining


Each event is associated with Case Identifiers(CaseID).




Additionally, each such event may include a uniqueidentifier.

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Process Mining

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Process Mining

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Process Mining


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Process Mining







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Process Mining

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Process Mining

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Process Mining


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Process Mining







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Process Mining

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Process Mining

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Process Mining


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Process Mining







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Process Mining

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Process Mining

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Process Mining


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Process Mining







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Process Mining

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Process Mining

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Process Mining


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Process Mining

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Process Mining

3 types of process mining techniques:1. Process Discovery

2. Process Conformance

3. Process Enhancement

3 types of process mining perspectives:1. Control Flow Perspective

2. Organizational Perspective

3. Case Perspective.

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Similar Task Algorithm

Similar Task algorithm focuses on identifying actorsperforming similar activities in the organizationalperspective.

It focuses on activities the actors perform irrespective ofcases.

It is based on the notion that people doing similar thingshave a stronger relation than people doing different things.

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Process Mining

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Process Mining

Case Identifier

Activity Identifier

Actor

1 A Nidhi

2 A Nidhi

2 C Kunal

1 B Priyanka

3 A Pooja

1 C Nidhi

3 D Kunal

3 B Priyanka

2 B Pooja

2 D Astha

1 D Astha

A B C D

Nidhi 2 0 1 0

Kunal 0 0 1 0

Priyanka 0 2 0 0

Pooja 1 1 0 0

Astha 0 0 0 2

Actor-Activity Matrix

Sample Event LogTable 2:

Table 3:

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Similar - Task Algorithm

Given two vectors of attributes, A and B, the Cosine-Similarity if given by

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Process Mining

Nidhi Kunal Priyanka Pooja Astha

Nidhi --- 0.32 0.00 0.63 0.00

Kunal 0.32 --- 0.00 0.00 0.70

Priyanka 0.00 0.00 --- 0.70 0.00

Pooja 0.63 0.00 0.70 --- 0.00

Astha 0.00 0.70 0.00 0.00 ---

Cosine Similarity ValuesTable 4:

Figure taken from [21].

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Similar Task Algorithm at a glance!

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Similar - Task Algorithm at a glance!

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Sub Contract Algorithm

Sub Contract algorithm focuses on how work moves amongperformers.

The main idea is to count the number of times individual jperforms an activity in between two activities performed byindividual i.

The relation between individuals are case dependent.

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Process Mining

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Process Mining

Case Identifier

Activity Identifier

Actor

1 A Nidhi

2 A Nidhi

2 C Kunal

1 B Priyanka

3 A Pooja

1 C Nidhi

3 D Kunal

3 B Priyanka

2 B Pooja

2 D Astha

1 D Astha


Case Identifier

Activity Identifier

Actor

1 A Nidhi

1 B Priyanka

1 C Nidhi

1 D Astha

2 A Nidhi

2 C Kunal

2 B Pooja

2 D Astha

3 A Pooja

3 D Kunal

3 B Priyanka

Organized Event LogTable 6:

Zoom Shape 1

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Process Mining

Case Identifier

Activity Identifier

Actor

1 A Nidhi

2 A Nidhi

2 C Kunal

1 B Priyanka

3 A Pooja

1 C Nidhi

3 D Kunal

3 B Priyanka

2 B Pooja

2 D Astha

1 D Astha


Case Identifier

Activity Identifier

Actor

1 A Nidhi

1 B Priyanka

1 C Nidhi

1 D Astha

2 A Nidhi

2 C Kunal

2 B Pooja

2 D Astha

3 A Pooja

3 D Kunal

3 B Priyanka

Organized Event LogTable 6:

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Sub - Contract Algorithm

normal = 4.0

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Process Mining


Nidhi 0.00 0.00 1.00 0.00 0.00

Kunal 0.00 0.00 0.00 0.00 0.00

Priyanka 0.00 0.00 0.00 0.00 0.00

Pooja 0.00 0.00 0.00 0.00 0.00

Astha 0.00 0.00 0.00 0.00 0.00

Sub Contraction Valuesbefore Normalization

Table 7:


Nidhi 0.00 0.00 0.25 0.00 0.00

Kunal 0.00 0.00 0.00 0.00 0.00

Priyanka 0.00 0.00 0.00 0.00 0.00

Pooja 0.00 0.00 0.00 0.00 0.00

Astha 0.00 0.00 0.00 0.00 0.00

Sub Contraction Valuesafter Normalization

Table 8:

Zoom Shape 1

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normal = 4.0

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Process Mining


Nidhi 0.00 0.00 1.00 0.00 0.00

Kunal 0.00 0.00 0.00 0.00 0.00

Priyanka 0.00 0.00 0.00 0.00 0.00

Pooja 0.00 0.00 0.00 0.00 0.00

Astha 0.00 0.00 0.00 0.00 0.00


Table 7:


Nidhi 0.00 0.00 0.25 0.00 0.00

Kunal 0.00 0.00 0.00 0.00 0.00

Priyanka 0.00 0.00 0.00 0.00 0.00

Pooja 0.00 0.00 0.00 0.00 0.00

Astha 0.00 0.00 0.00 0.00 0.00


Table 8:

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normal = 4.0

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Process Mining


Nidhi 0.00 0.00 1.00 0.00 0.00

Kunal 0.00 0.00 0.00 0.00 0.00

Priyanka 0.00 0.00 0.00 0.00 0.00

Pooja 0.00 0.00 0.00 0.00 0.00

Astha 0.00 0.00 0.00 0.00 0.00


Table 7:


Nidhi 0.00 0.00 0.25 0.00 0.00

Kunal 0.00 0.00 0.00 0.00 0.00

Priyanka 0.00 0.00 0.00 0.00 0.00

Pooja 0.00 0.00 0.00 0.00 0.00

Astha 0.00 0.00 0.00 0.00 0.00


Table 8:

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Sub Contract Algorithm at a glance I

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Sub - Contract Algorithm at a glance!

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Sub Contract Algorithm at a glance II

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Sub - Contract Algorithm at a glance!

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Query languages provide the most standard way tointeract with the database.

We, try to implement process mining algorithm usingdatabase query languages to the extent possible so thatour application is tightly coupled to the database.

Our work lies at the intersection of Process Mining andNoSQL databases.

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Research Aim

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Research Aim .

To investigate the intersection of Process Mining and Graph Database(s) fordetecting social, hierarchical structures.

To understand application needs that can be modelled into this new domain.

To implement Similar-Task algorithm and Sub-Contract algorithm in row-orienteddatabase, MySQL.

To implement Similar-Task algorithm and Sub-Contract algorithm in graphoriented database, Neo4j.

To compare performance of Similar-Task algorithm and Sub-Contract Algorithm in

MySQL and Neo4j.

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3. Implementation of Similar-Task and Sub-Contract Algorithm in SQL, RDBMS




7. Conclusion

8. Limitations

9. References


Vishleshan

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Implementation of Mining Algorithms in Relational Databases

Ordonez et al. [5] Implement k-means clustering algorithm in SQL.

Cluster large datasets in RDBMS.

Define suitable tables, index them and write suitable queries forclustering purposes.

Ordonez et al. [6] Extend own work in [5].

Efficient implementation of EM algorithm to perform clustering invery large datasets.

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Related Work and Novel Research Contributions

Implementation of Mining Algorithms in Relational Databases.

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Berzal et al. [7] Implemented Tree Based Association Rule Mining to discover

interesting patterns in relational databases.

Sattler et al. [8] Applied data mining techniques on a decision tree and classifier.

Tight coupling of data mining and database systems.

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Implementation of Mining Algorithms in Graph Databases

Wang et al. [9] Studied structural pattern mining for large disk based graph

databases.

They presented a novel ADI index structure and efficient algorithmsfor mining frequent pattern.

Wang et al. [10] Presented techniques to obtain scalable mining in graph databases.

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Implementation of Mining Algorithms in Graph Databases.

Huan et al. [11] Presented novel technique to mine maximal frequent sub-graph in

graph databases.

Ozaki et al. [12] Came up with hyper-clique pattern in graph databases.

Used hyper-clique pattern to detect highly correlated sub-graphs.

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Performance Comparison of Mining Algorithms in Relational and Graph Databases.

Vicknair et al. [13] Performance comparison of Relational and Graph databases for

data provenance systems.

McColl et al. [14] Evaluated performance of series of open-source graph databases.

Used various graph algorithms for a graph setup consisting of 256 million nodes.

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Ciglan et al. [15] Benchmarked graph databases over graph traversal algorithms.

Macko et al. [16] Presented a performance introspection framework for Graph

database, PIG.

PIG provided tools and mechanisms to understand performance of graph database.

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Novel Research Contributions

While there has been work done in implementing data mining algorithmsin relational and graph databases, we are,

First to implement organizational mining algorithms (Similar-Task andSub-Contract) in row oriented database MySQL using SQL.

First to implement organizational mining algorithms (Similar-Task andSub-Contract) in graph oriented database Neo4j using CYPHER.

Performance Benchmarking of organizational mining algorithms(Similar-Task and Sub-Contract) on MySQL and Neo4j.

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Novel Research Contributions.

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3. Implementation of Similar-Task and Sub-Contract Algorithms in SQL, RDBMS




7. Conclusion

8. Limitations

9. References

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Implementation of Similar-Task and Sub-Contract Algorithm in SQL, RDBMS

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Steps

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Implementation of Similar-Task algorithm in SQL can be divided into four (4) broad tasks

Declare and iterate cursor to select distinct tasks.

Create a table to store result.

Fetch actors vector and calculate Cosine Similarity.

Write results to the result table.

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Define and iterate cursor

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Declare cursor to select distinct tasks from table

Open cursor. Loop through the results returned by the cursor.

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Declare table to store results

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Dynamically create table with the specified table-name.

Prepare SQL statements from the query and execute it.

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Fetch actors vector and calculate Cosine-Similarity I.

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Prepare query to insert into table

Define variables to store values for cosine-similarity calculation.

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Fetch actors vector and calculate Cosine-Similarity II.

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Inside the cursor, collect distinct tasks from the tables for the required calculation.

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Fetch actors vector and calculate Cosine-Similarity III.

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Append parts of cosine similarity calculation to the SQL query.

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Update Final Results I.

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Declare a cursor to get all distinct teams.

Iterate through the cursor to get distinct teams

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Update Final Results II.

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Form a query by for creating table and taking distinct teams as columns.

Inside the cursor loop, append distinct teams as columns of the table.

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Update Final Results III.

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Form a query for inserting values into the table (resultant table)

Inside the cursor loop, assign similarity values at the respective column (match teams).

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Steps

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Sub - Contract Algorithm.

Sub-Contract Algorithm implementation can be studied under four (4) broad categories:

Create table to store results.

Find distinct case identifiers.

Update normal and find sub-contraction within each case.

Normalize the result.

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Create table to store results I

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Declare cursor to select distinct actors.

Iterate through the cursor to collect the distinct actors.

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Create table to store results II

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Form a query to create a table.

Inside the cursor, append each distinct actor as part of the query.

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Find distinct case identifiers

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Declare cursor to select distinct case identifiers with count >= 3

Iterate through the cursor. For each distinct case identifier, call procedure ExecuteCase.

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Update normal and find sub-contraction I.

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Update normal.

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Update normal and find sub-contraction II.

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Declare a cursor to find sub-contracting actors.

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Update normal and find sub-contraction III.

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Iterate through the cursor to find IDs of actor

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Update normal and find sub-contraction IV.

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Declare cursor to find sub-contracting actors.

Iterate through the cursor to find IDs of sub-contracting actors.

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Update normal and find sub-contraction V.

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For any pair of sub-contracting actor, insert or update sub-contract value between them.

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Normalize the result.

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Declare cursor to select distinct actors that formed columns of the result table

For each column, form an update query and normalize it by normal

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7. Conclusion

8. Limitations

9. References

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Implementation of Similar-Task and Sub-Contract Algorithm in CYPHER, Graph Oriented

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Steps

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Implementation of Similar-Task and Sub-Contract Algorithm in CYPHER, Graph Oriented.

Similar Task Algorithm.

Implementation of Similar Task algorithm in CYPHER consists mainly of two (2) broad functions.

Load data with Actor and activity nodes being unique.

Calculate Cosine-Similarity between actors.

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Load actor and activity node uniquely.

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Load data directly from the data file. Make unique nodes for actor and activity.

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Calculate Cosine - Similarity.

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Match common activities between actors and calculate similarity.

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Steps

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Sub Contract Algorithm.

Implementation of Sub Contract algorithm in CYPHER consists mainly of four (4) broad functions.

Identify sub contracting actors within each case.

Collect unique names and make new nodes for each of them.

Set sub contraction strength between unique actor nodes.

Calculate normal and normalize the sub contraction value.

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Identify sub contracting actors.

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Identify sub-contracting actors and connect then via [:RELATED_TO] relationship.

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Collect unique names and create unique actor nodes.

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Collect unique actor names

Make new nodes, UNIQUEACTOR for each distinct actor names found.

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Set sub contraction strength between unique actors.

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For all sub-contracting actor, determine strength of sub-contraction between the actors.

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Calculate normal and normalize the result.

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Calculate normal.

Normalize the sub-contraction strength between actors.

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7. Conclusion

8. Limitations

9. References

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Experimental Dataset

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Experimental Dataset.

We use Business Process Intelligence 2014 (BPI 2014)dataset to conduct our experiments.

The log contains events from an incident and problemmanagement system of Rabobank Group ICT.

Contains data about managing requests from RabobankGroup ICT.

Contains total 466737 records.

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Dataset Details

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Fig. 11: Sample Event Log from MySQL.

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7. Conclusion

8. Limitations

9. References

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Performance Comparison

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Load Time

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Dataset size Load Time (msec)

MySQL Neo4j

65,000 2467 3413

1,01,000 2875 3362

2,19,500 5966 4354

3,00,000 5850 5877

4,66,737 7819 6875

Table 9: Data Load TimeFig 12: Load Time

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Execution Time I

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Table 10: Execution Time of Step-8 & Step-9

Dataset Size

Execution Time (msec)

Step -8 Step -9

MySQL Neo4j MySQL Neo4j

65,000 225 9616 2467 2403

1,01,000 372 11700 2875 2925

2,19,500 713 14655 5966 3664

3,00,000 903 29520 5850 7380

4,66,737 1403 48891 7819 12223

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Execution Time II

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Fig. 13: Execution Time of Step-8 & Step-9

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Disk Usage in MySQL I

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Table 11: Disk Space Usage in MySQL.

Tables Dataset Size

65000 101000 219500 300000 466737

Dataset 3686400 5783552 11026432 15220736 21544960

OTMatrix 65536 65536 65536 81920 81920

InitSim 1589248 1589248 1589248 3686400 3686400

FinalSim 229376 262144 278528 491520 1589248

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Disk Usage in MySQL II

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Fig 14: Disk Space Usage in MySQL.

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Disk Usage in Neo4j I

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Table 12: Disk Space Usage in Neo4j.

Graph Elements

Dataset Size

65000 101000 219500 300000 466737

Nodes 2820 2910 3075 3990 4215

Relationships 770040 414315 479663 8568809 983227

Properties 1033856 563873 651203 1155011 1323439

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Disk Usage in Neo4j II

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Fig. 14: Disk Space Usage in Neo4j.

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Load Time

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Dataset size Load Time (msec)

MySQL Neo4j

65,000 6575 9567

1,01,000 8390 10476

2,19,500 14279 14873

3,00,000 26437 25435

4,66,737 43712 38234

Table 13: Load TimeFig 15: Load Time

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Execution Time in MySQL I

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Table 14: Execution Time for 4 main steps in MySQL.

Dataset Size Execution Time (msec)

Update Normal

Sub-ContractDetection

Update Result

Normalize result

65,000 32 11712 8296 16

1,01,000 32 11782 8138 16

2,19,500 35 11713 7940 17

3,00,000 70 11,736 8094 17

4,66,737 73 11747 7754 20

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Execution Time in MySQL II

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Fig 16: Execution Time for 4 main steps in MySQL.

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Execution Time in Neo4j I

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Table 15: Execution Time for 4 main steps in Neo4j

Dataset Size Execution Time (msec)

Update Normal

Sub-ContractDetection

Update Result

Normalize result

65,000 118 1542 2077 5

1,01,000 140 1707 2773 5

2,19,500 202 2534 2369 6

3,00,000 336 3442 5261 9

4,66,737 560 4149 5334 9

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Execution Time in Neo4j II

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Fig. 17: Execution Time for 4 main steps in Neo4j.

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Disk Space Usage in MySQL I

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Tables Dataset Size

65000 101000 219500 300000 466737

Dataset 4734976 6832128 13123584 18366464 27836416

OrganisedData 4734976 6832128 13123584 18366464 27836416

ResultMatrix 1589248 1589248 1589248 1589248 1589248

Table 15: Disk Space Usage in MySQL

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Disk Space Usage in MySQL II

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Fig 17: Disk Space Usage in MySQL

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Disk Space Usage in Neo4j I

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GraphElements

Dataset Size

65000 101000 219500 300000 466737

Nodes 982212 1523732 3360798 4598454 7190330

Relationships 153477921 183955761 285778449 375437997 490033038

Properties 384189475 461537287 719874720 942665404 1238579332

Table 16: Disk Space Usage for graph elements in Neo4j.

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Disk Space Usage in Neo4j II

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Fig. 18: Disk Space Usage for graph elements in Neo4j.

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7. Conclusion

8. Limitations

9. References

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Conclusion

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Conclusion

Neo4j performs better when it comes to loading data.

Read operations in MySQL are comparatively faster for asingle node setup.

Neo4j gives much improved performance wheneverrelationships are of prime importance.

Writes performance varied greatly for both cases. Forsmaller dataset, MySQL performs better whereas for largerdataset, Neo4j gives improved performance.

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Conclusion

.

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7. Conclusion

8. Limitations and Future work

9. References

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Limitations

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Limitations

Different sizes of single dataset was used.

Single node setup of databases were used.

Metrics used for organizational mining were only two innumber.

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Limitations and Future Work

. Limitations

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Future Work

To apply the algorithm over larger data sets.

Create a multi-node Neo4j setup and implement thealgorithms on it.

Implement and study impact of process enhancement andrecommendation systems.

Experiment with more relational and graph orienteddatabases.

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Limitations and Future Work.

Future Work

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7. Conclusion

8. Limitations

9. References

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Implementation of Similar-Task and Sub-Contract Algorithm in CYPHER, Graph Oriented

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References I

Vishleshan

References

WIL VAN DER AALST.Process Mining: Overview and Opportunities.ACM, 2012. vi, 2, 11

P Neubauer. Graph databases, NOSQL and Neo4j? www.infoq.com.

I Robinson, J Webber, E Eifrem. Graph Databaseswww.books.google.com.

Minseok Song, WIL M. P. Van Der Aalst. Towards comprehensive support for organizational mining.Elsevier, 2008.

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References II

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References

Carlos Ordonez.Programming the K-means clustering algorithm in SQL

C. Ordonez and P. Cereghini.SQLEM: fast clustering in SQL using the EM algorithm.International Conference on Management of Data

Nicolas Marin Jose Maria Serrano Fernando Berzal, Juan Carlos Cubero.TBRAR: An ecient method for association rule mining in relational databases.Elsevier, 2001.

K-U.Sattler and O.Dunemann.SQL Database Primitives for Decision Tree Classiers.Conference on Information and Knowledge Management, 2001.

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References III

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References

W Wang, C Wang, Y Zhu, B Shi, J Pei, X Yan.Graphminer: a structural pattern mining system for large disk based graph databases and its applications.ACM, 2005.

C Wang, W Wang, Y Zhu, B Shi, J Pei.Scalable Mining of large disk based graph databases.ACM, 2004.

J Huan, W Wang, J Prins.SPIN: mining maximal frequent subgraphs from graph databases.ACM, 2004.

T Ozaki, T Okhwaha.Mining correlated subgraphs in graph databases.Advancement in Knowledge Discovery and Data Mining, 2008.

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References IV

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References

C Vicknair, M Macais, Z Zhao, X Nan, Y Chen.A comparison of graph databases and a relational database: a data provenance perspectiveACM, 2010.

RC McColl, R Ediger, J Poovey, D Campbell.A performance evaluation of open-source graph databases.ACM, 2014.

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Vishleshan

References

Why NOSQL?Couchbase.

Scale-out vs. Scale-up.www.natishalom.typepad.com.

Introduction to Graph Databases and Neo4j.www.neo4j.com

Cosine- Similaritywww.Wikipedia.com

From Relational to Neo4j.www.neo4j.com

jeevan-mtech-thesis-presentation.pdf

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