An Open Source System for Big Data Warehousing
Nunziato Cassavia
, Elio Masciari
and Domenico Sacc
ICAR-CNR, Rende, Italy
Centro di Competenza ICT-SUD, Rende, Italy
Big Data Warehousing, NoSQL and Mondrian.
The pervasive diffusion of new data generation devices has recently caused the generation of massive data
flows containing heterogeneous information generated at different rates and having different formats. These
data are referred as Big Data and require new storage and analysis approaches to be investigated for managing
them. In this paper we will describe a system for dealing with massive big data stores. We defined an open
source tool that exploits a NoSQL approach for data warehousing in order to offer user am intuitive way to
easily query data that could be quite hard to be understood otherwise.
The increasing availability of huge amounts of data
from heterogeneous sources, calls for the definition
of new paradigms for their management this prob-
lem is known with the name Big Data (Nature, 2008;
Economist, 2010; Economist, 2011; Agrawal et al.,
2012; Lohr, 2012; Manyika et al., 2011; Noguchi,
2011a; Noguchi, 2011b; Labrinidis and Jagadish,
2012). As a consequence of new perspective on data,
many traditional approaches to data analysis result
inadequate both for their limited effectiveness and
for the inefficiency in the management of the huge
amount of available information. Therefore, it is nec-
essary to rethink both the storage and access patterns
to big data as well the design of new tools for data
presentation and analysis. In particular, On Line An-
alytical Processing (OLAP) tools require suitable ad-
justments in order to work for big data processing ef-
fectively. Indeed, it is crucial, during the construction
and analysis of a data warehouse, to exploit ad-hoc
tools that allow an easy and fast search of data stored
in several nodes distributed over the storage layer.
More in detail, while building a data warehouse
for Big Data, the key to a successful analysis (i.e. a
fast and effective one) is the availability of good in-
dexing mechanisms. Therefore, an additional cost in
terms of storage space consumption needed for stor-
ing the appropriate indices is to be taken into account.
It is worth noticing that the problem of fast ac-
cessing relevant pieces of information arises in sev-
eral scenarios such as world wide web search, e-
commerce systems, mobile systems and social net-
works analysis to cite a few.
Successful analyses for all the application con-
texts rely on the availability of effective and efficient
tools for browsing data so that users may eventually
extract new knowledge which s/he was not interested
In this paper, we describe the architecture of an
open source system for big data OLAP, capable to
deal with Big Data and offering the chance to “nav-
igate through” the data in a simplified manner, while
keeping traditional operators available in an OLAP
based system such as roll-up, drill-down, slice and
dice. Our system is based on the reliable and widely
used MonetDB NOSQL data store and the Mondrian
analysis tool (thus we named it MonOLAP) as will be
described in next sections.
In this section, we will describe our system design. As
our goal is the development of an Open Source tool
that takes advantage of both Mondrian and MonetDB
systems, we leverage the abstraction features offered
by Mondrian and the data storage layer functionali-
ties of MonetDB. Our system assumes that the tar-
get data could be stored on some external relational
Cassavia, N., Masciari, E. and Saccà, D.
An Open Source System for Big Data Warehousing.
DOI: 10.5220/0006485703060313
In Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), pages 306-313
ISBN: 978-989-758-255-4
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: Mondrian Architecture.
DBMS. Thus, the system allows the user to select her
own interesting data from relational datasources and
the possibility to create, from the selected data, her
custom multidimensional datacubes.
In order to load the data into the MonetDB repos-
itory, it is mandatory to perform a transformation and
migration step. Moreover, the definition of multi-
dimensional datacube will be tailored to support the
same multidimensional view on MonetDB own data
structures. The transformation of the original data
affects the materialization of the datacube as well as
data flattening. The latter step involves the denormal-
ization of the OLAP schema fact table, i.e., we add to
the fact table the attributes related to the measure of
interest declared in the OLAP schema.
The outcome of this preliminary operation is a
performance improvement, as the increase in the
number of attributes in the fact table will perfectly
fit the MonetDB column management strategy. The
performance improvement obtained by this synergy
between the two systems will be clearer from the ex-
perimental evaluation shown in next sections. Figure
1 shows the block diagram of the architecture of Mon-
drian OLAP system.
We briefly summarize here the main Mondrian
features we exploited in our prototype implementa-
the system allows data cube querying using MDX
language by a simple user interface. In order to
properly perform the queries, the definition of an
XML file representing the datacube and its dimen-
sions, hierarchies and measures, is mandatory;
the MDX query is forwarded to the OLAP engine
that perform the transformation to SQL and exe-
cute the queries to the repository containing the
the query result is sent to Mondrian for user de-
fined processing and then sent to the user interface
which displays the results in a multidimensional
way using interfacing-dependent mechanisms.
In Figure 2 we report the overall architecture of
our MonOLAP engine. It contains several modules
that have been used to exploit MonetDB features to
create a high-performance still reliable system. More
in detail, two modules plays a crucial role in the sys-
tem architecture, namely the ETL tool and the DBMS
MonetDB which are responsible respectively for ma-
nipulating the data and storing the data in flattened
The ETL tool deals with:
extracting data from the data source (that we recall
could be an external DBMS);
materializing the OLAP cube;
transforming the cube by flattening it;
transferring the data to the MonetDB storage
It is worth noticing that, the configuration file that
defines the cube dimensions, measures, and layers
(that refers to the original DBMS) has to be built ac-
cording to the warehouse architect specification for
the domain being analyzed. The obtained XML file,
defined as Adapted Cube Descriptor, is the result of
a transformation that takes into account the new flat-
tened schema for data materialization.
For the sake of completeness, we report in Figure
3 the workflow for OLAP cube definition that is per-
formed by user using the Schema Workbench tool.
As explained above, our system is an integrated en-
vironment for supporting OLAP data analysis for Big
Data. The novelty of our approach mainly consists
in its easy features for data manipulation and their
transfer to/from any Relational DBMS selected by the
user. However, after an extensive set of experiments,
we found that optimal performances can be obtained
by leveraging MonetDB. We also manage the data
mapping phase in order to leave the multidimensional
An Open Source System for Big Data Warehousing
Figure 2: MonOLAP Engine Architecture.
Figure 3: OLAP cube definition by Schema Workbench.
view unchanged while using the MonetDB data struc-
tures. This results in a high performance gain due to
the fact that MonetDB is extremely efficient in man-
aging tables with a large number of attributes. Based
on this preliminary observation, the name MonOLAP
was chosen to recall its peculiar features deriving
from the integration of Mondrian with MonetDB for
OLAP analysis ((Mon)drian + (Mon)etDB for OLAP
= MonOLAP).
As introduced in the high level description of the
system, the MonOLAP System consists of 3 software
Schema Workbench Plus.
While MonetDB and Mondrian are respectively
the Storage Layer and OLAP Server of the system,
the last module, Schema Workbench Plus, can be de-
fined as a cube designer tailored for date cube defini-
tion and several forms of materialization through ETL
More in detail, the main features of the above
mentioned modules, that have been extensively used
can be summarized as follows:
Schema Workbench Plus.
Creation or loading of the XML schema rep-
resenting a cube: after defining the connection to
the source DBMS, the user can create and vali-
date an XML schema for a given cube or alterna-
tively can also update an existing schema. The
validation of the schema is performed by inter-
acting with the data repository (by checking the
match of the fact table and dimensions with the
input data sources);
Materialization of data cube on the storage
layer: the user, after selecting one of the cubes
of the schema, can perform the materialization on
the storage layer. The system offers the user the
possibility to choose, depending on her needs, the
type of materialization.
After configuring Mondrian configuration
parameters, i.e. the connection to the DBMS and
the XML schema path, the user can query the data
cube in graphical mode and then obtain a multidi-
mensional view of the data. Schema Workbench
Plus plays a crucial role at this stage, since it is
completely transparent to the user who is able to
generate a XML scheme of the materialized cube
which allows the query engine to answer to the
same MDX queries of the original data cube.
The cube data description is stored by Schema
Workbench Plus in XML format. The materialization
of the cube causes the execution of two operations:
the materialization on the storage layer, which can
be of two types, flattened or starred;
the generation of the XML schema for the materi-
alized cube.
In this section we will explore the implemented mate-
rialization techniques.
4.1 Flat Schema
Algorithm 1 illustrates the materialization algorithm
for a Flat Schema. The algorithm takes as input two
parameters, i.e., the data cube to be materialized (C)
and the table for materializing the datacube (F).
The materialization step requires a preliminary
check on the type of table of the data cube. When
defining an OLAP data cube, user has to specify the
fact table for measure computation. The peculiarity
KomIS 2017 - Special Session on Knowledge Discovery meets Information Systems: Applications of Big Data Analytics and BI -
methodologies, techniques and tools
Algorithm 1 Flat Schema Algorithm.
1: procedure FLAT SCHEMA
2: C selected cube
3: F flatten table
4: if C. f actTable is a View then
5: calculate fact table from SQL expression
6: calculate fact attribute from SQL expression
7: else
8: get fact table from cube
9: for m in C.measure do
10: if m is SQL Expression then
11: Calculate Attribute
12: Add Attribute to Flatten Table
13: else
14: Add Attribute to Flatten Table
15: for d in C.dimension do
16: Select d attributes from dimension table
17: F join(fact table, dimension table)
of this table relies on the fact that it can be either a
table on the database or a multi-table view. In the
case of a multi-table view, we need to extrapolate the
fact table by keeping track of the attributes to be se-
lected because they will then be routed to the flatten
table. Once the fact table has been defined, we need
to check how the user defined the cube dimensions.
Measures on a given cube may refer to a specific col-
umn of the fact table or it may be the result of an SQL
expression that involves multiple columns in the fact
table. Then, before adding the attribute measure to
the flatten table, it is necessary to compute the SQL
expression . Finally, it is necessary to tweak all the
dimensions of the data cube by performing the join
operation between the fact table and the dimension
table in order to update the flatten table with all the
columns that affect the chosen dimensions.
4.1.1 Star Schema
Star schema materialization can be performed by ap-
plying flat materialization to each dimension. In Al-
gorithm 2, we report the preudo code of this algo-
As in the previous case, the key operation to be
performed is fact table and measures identification,
and then the partial materialization can take place as
he fact table that contains all measures and all external
reference keys to the dimension tables. A new table
is created for each dimension, which contains all the
attributes of interest for the dimension being materi-
Algorithm 2 Star Schema Algorithm.
1: procedure STAR SCHEMA
2: C selected cube
3: T fact table
4: if T is a View then
5: calculate fact table from SQL expression
6: calculate fact attribute from SQL expression
7: add all foreign keys to fact table
8: materialize fact table
9: else
10: get fact table from cube
11: add all foreign keys to fact table
12: materialize fact table
13: for m in C.measure do
14: if m is SQL Expression then
15: Calculate Attribute
16: Add Attribute to Fact Table
17: else
18: Add Attribute to Fact Table
19: for d in C.dimension do
20: Select d attributes from dimension table
21: Create new table D
22: D join(fact table, dimension table)
In this section, we describe the results we obtained
by running MonOLAP on TETRA network data
log. TETRA (TErrestrial Trunked RAdio, originally
Trans-European Trunked RAdio) is a commercial ra-
dio waveform standard with mobile and portable sys-
tems used mainly by public security, military agen-
cies and emergency services as well as by private civil
services. TETRA is a set of standards for private
telecommunication systems targeted to a professional
user, but also for service providers interested in hav-
ing their own radio network.
TETRA is the first open standard for profes-
sional digital radio systems and its services are pro-
vided with a whole range of NE elements (Network
Elements) that supply the management system and
support real-time operation and maintenance, neces-
sary to guarantee the daily operation of the system.
TETRA provides a communication system for several
usage scenarios. A call log is generated for each gen-
erated call within the TETRA Network. This call log
provides call details, including the parties involved in
the call, their location, call duration, and resource us-
age. For many activities, the full log of a call is built
by a set of small call logs that are generated by the var-
ious Control Nodes within network. A context log is
An Open Source System for Big Data Warehousing
generated for each data packet that can provide infor-
mation about activating and deactivating a data packet
and the amount of data packets that are transferred in
both directions between the mobile and the Control
5.1 Call Logs Database on TETRA
The database contain the real logs generated on
TETRA network in the Emilia Romagna region. This
database is a relational database of more than 30GB
size containing multiple information on each teth-
ered call. The database was built using the Firebird
RDBMS and has the following features:
124 tables;
2000 attributes ;
20 tables for storing log information;
70 attributes for each log table (on average);
30 Gigabytes of data, of disk occupation;
132.165.089 tuples.
We have first identified a fact of interest in order
to define a Data Mart for OLAP analysis. We have
chosen to locate logs for calls that generated alerts,
that is, those for which the call was interrupted or dis-
connected for external cause.
5.2 Logical Model of the OLAP Cube
The source database is very heterogeneous and con-
tains many unnecessary data for the analysis to be
carried out. For this reason, it was crucial, through
ETL procedures, to extract the data log of interest. In
order to perform these transformations, it is necessary
to define in the measures of interest, dimensions and
hierarchies that we want to analyze. The conceptual
model used to represent the information schema is the
Dimensional Fact Model, due to some privacy con-
straints, we limited our analysis to number of calls
As regards the analysis dimensions, we identified
the following:
CALL END REASON (cardinality 23). Indi-
cates the reason for call ending, this type of
information is already classified in the original
database in a table that lists 23 reasons why a
TETRA network call may end;
DISCONNECT CAUSE (cardinality 203). in-
dicates the reason why an user has been discon-
nected from the network, differently from the rea-
son for ending a call, this dimension states why it
Figure 4: Dimensional Fact Model for OLAP Analisys on
Tetra Network.
is disconnected from the TETRA network, it con-
tains 203 causes of disconnection;
SCN (cardinality 2). as the data comes from a
log database on TETRA Network of Emilia Ro-
magna, there is no reason to have a territorial di-
mension, except for the SCN to which the inter-
rupted call refers. Analysis of the source database
we found that all data refers only to two SCNs:
one located in the municipality of Bologna and
one in the municipality of Faenza;
TEMPORARY DIMENSION (cardinality 8760
as this number refers to the hours in one year).
logs are stored in detail per millisecond, but there
is no practical need to aggregate the data till such
finer granularity then we decided to support the
analysis of the temporal dimension hierarchically
by hours, days and months. The year does not
make sense in our experimental analysis as the
data refer to 2010. Also we want to be able to ag-
gregate for: days in week and weekend, day of the
week, time zone and day type. By analyzing the
data based on this model we will be able to under-
stand on which days network problems occurred,
for what reasons user have been disconnected, for
what reasons the calls have been terminated and
on what SCN, for which time slot, for which days
of the week and so on.
The Dimensional Fact Model referring to the data
model for this OLAP analysis is shown in 4.
Using ETL procedures, the table of facts and di-
mensions was extracted from the original database
and uploaded also to MySQL DBMS for the sake of
comparison. The Star Schema, representing the DFM
for TETRA logs on MySQL, is shown in 5
5.2.1 Experimental Settings
Using MonOLAP, with the Schema Workbench
PLUS component on the schema described in Figure
5, the following schemes are obtained:
Star Schema on MySQL
Flat Schema on MonetDB
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methodologies, techniques and tools
Figure 5: Star Schema for OLAP Analisys on Tetra Net-
Star Schema on MonetDB
Flat Schema on MySQL
Our test bench is composed by the following
Query 1. Drill Down on SCN dimension
Query 2. Drill Down on dimensions SCN and
Query 3. Drill Down on dimensions CALL END
Query 4. Drill Down on TIME dimension
Query 5. Drill Down on dimensions TIME and
Query 6. Drill Down on SCN and TIME dimen-
In the following, we report the queries on the
OLAP cube expressed in MDX Language:
Query 1.
select {[Measures].[numerolog]}
from [traffic]
Query 2.
select {[Measures].[numerolog]}
from [traffic]
Query 3.
select {[Measures].[numerolog]}
from [traffic]
Query 4.
select {[Measures].[numerolog]}
from [traffic]
Query 5.
select {[Measures].[numerolog]}
An Open Source System for Big Data Warehousing
from [traffic]
Query 6.
select {[Measures].[numerolog]}
from [traffic]
5.2.2 Experimental Results
The execution of the queries described above had dif-
ferent execution times depending on the execution
schema as reported in Figure 6 (each cell represents
the execution time of the query in milliseconds).
Figure 6: Query Execution Times.
The highest performance gap has been obtained
when comparing MySQL Star Schema and MonetDB
Flat Schema as reported in Figure 7.
The above mentioned difference is even more ac-
centuated when considering the results shown in Fig-
ure 8.
The denormalization process, which causes Mon-
etDB on a flatten schema to have better performance,
does not lead to the same results when using MySQL.
Indeed, we can observe that MySQL does not always
take advantage of the denormalization performed. In
Figure 9, it is easy to see that the MySQL execu-
tion times increase despite the denormalization on
On MonetDB, the flattening process guarantees
the best experimental performances, this is due to
Figure 7: All Queries Result Times on different Schemes.
Figure 8: MySQL Star Schema vs MonetDB Flat Schema.
Figure 9: Comparison between MySQL Star Schema and
MySQL Flat Schema.
Figure 10: Comparison between MonetDB Star Schema
and MonetDB Flat Schema.
KomIS 2017 - Special Session on Knowledge Discovery meets Information Systems: Applications of Big Data Analytics and BI -
methodologies, techniques and tools
Figure 11: Comparison Flat Schema between MySQL and
Figure 12: Query 5 an all scheme execution times.
MonetDB’s internal operation, as can be seen from
Figure 10. MonetDB with the same denormalized
schema offers, compared to MySQL, better perfor-
mance, as shown in Figure 11. This peculiarity is
most relevant when comparing the results on the same
query. Figure 12 takes into consideration the Query 5
which involves join operations with high cardinality.
Big data analysis is a challenging task as we need
to take into account the velocity, variety and volume
of information to be analyzed. Indeed, such features
heavily influence the design of a system for big data
warehousing. In this respect, we analyzed several de-
sign options in order to implement a prototype for
Big Data Warehousing. Our MonOLAP prototype has
been used for TETRA net analysis. Results on the
efficiency of the system were quite satisfactory. We
are now gathering real data from other public sources
in order to perform a detailed analysis of the accu-
racy and effectiveness we can obtain by leveraging
our prototype.
This work was supported by MIUR Cybersecurity
project. We also thank Gaetano Fabiano for its con-
tribution to system implementation.
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An Open Source System for Big Data Warehousing