Embed Size (px)
Citation preview
DEVELOPMENT OF REAL-TIME OLAP
ALGORITHM USING MULTICORE DISTRIBUTED
PROCCESSING
BY
HAYTHAM I. M. ALZEINI
A dissertation submitted in fulfilment of the requirement
for the degree of Master of Science in Computer and
Information Engineering
Kulliyyah of Engineering
International Islamic University Malaysia
AUGUST 2014
ii
ABSTRACT
Online analytical processing (OLAP) is becoming increasingly essential technique,
particularly for decision support systems (DSS). OLAP is considered a suitable
technology for online analysis, in comparison to its counterpart: Online transaction
processing (OLTP), due to the fact that OLAP offers instantaneous answers to the
immediate queries that decision makers urgently need to make their decisions at some
critical moments based on the latest updates of the warehouse. However, despite its
speed processing capabilities; OLAP does not satisfy stringent Real-Time
applications’ requirements. Rather, current OLAP approaches the Real-Time. In other
words OLAP can achieve partial Real-Time results and the reset is materialized. Our
study addresses this shortcoming and attempts to propose a novel solution taking
advantage of revolutionary hardware development on two levels; namely, the multi-
core processors as well as distributed heterogeneous systems processing. This new
approach exploits the hardware resources optimally, and as a result; significantly
increases the processing speed. Our results have shown gain from 350% to 1200% in
terms of response time compared to our benchmark in which multi-core CPU only has
been utilized. In addition, the results have shown a propositionally increased gain with
increasing size of data due to the fact that the Graphical Processing Unit (GPU)
becomes more dominant component in the searching process as the data size
increases. We argue that with the new results, the heterogeneous solution is a very
strong candidate to our Real-Time OLAP problem
iii
ملخص البحث
أصبحت تقنية أساسية خصوصاً في أنظمة دعم (OLAP) كةالمعالجة التحليلية عبر الشبالقرار. المعالجة التحليلية عبر الشبكة هي تكنولوجيا مناسبة من أجل التحليل عبر الشبكة
ذلك نظراً للحقيقة القائلة بأن .(OLTP) مقارنة بنظيرتها معالجة التحويلية عبر الشبكةلحظية للاستعلامات الحالية التي يحتاجها صناع المعالجة التحليلية عبر الشبكة تقدم أجوبة
القرار بشكل عاجل لبناء قراراتهم في اللحظات الحرجة متظمنة كل تحديثات مخزن البياناتعلى الرغم من إمكانيات السرعة التي تتمتع بها المعالجة التحليلة عبر الشبكة، إلا أنها لا تحقق
دراستنا تناقش .ولكنها تقارب الزمن الحقيقيمتظلبات تطبيقات الزمن الحقيقي كاملة. العيوب والمحاولات التي قدمت من أجل تقديم حل جديد يأخذ في الحسبان التطور الثوري الطارئ على العتاد الصلب على مستويين اثنين: أولًا، المعالجات المتعددة الأنوية، ونظم
اد الصلب بشكل أمثل والنتائج تزيد المعالجة الهجينة. المقاربة الجديدة تستغل مصادر العت %0033على %053نتائج البحث أظهر ربح يعادل بشكل ملحوط سرعة الاستجابة
من حيث زمن الاستجابة مقارنة بالعمل السابق المعتمد فقط على المعالجات متعددة الأنوية. انات نتيجة بالإضافة إلى ذلك، أظهرت النتائج تزايد نسبي للربح طراً مع زيادة حجم البي
كنتيجة لذلك، . على عملية المعالجة والبحث GPU لزيادة سيطرة وحدة معالجة الصورفإننا نجادل في ضوء النتائج الجديدة أن استخدام الأنظمة الهجينة هي مرشح قوي لحل
.لية عبر الشبكة في الزمن الحقيقيمشكلة المعالجة التحلي
iv
APPROVAL PAGE
I certify that I have supervised and read this study and that in my opinion, it conforms
to acceptable standards of scholarly presentation and is fully adequate, in scope and
quality, as a dissertation for the degree of Master of Science (Communications
Engineering)
…………………………………..
Shihab A. Hameed
Main Supervisor
………………………………….
Mohamed H. Habaebi
Co-Supervisor
I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a
dissertation for the degree of Master of Science (Communications Engineering)
…………………………………..
Aisha Hassan Abdalla Hashim
Internal Examiner
…………………………………..
Azizah Binti Abdul Manaf
External Examiner
This dissertation was submitted to the Department of Electrical and Computer
Engineering and is accepted as a fulfilment of the requirement for the degree of
Master of Science (Communications Engineering)
…………………………………..
Othman O. Khalifa
Head, Department of Electrical
and Computer Engineering
This dissertation was submitted to the Kulliyyah of Engineering and is accepted as a
fulfilment of the requirement for the degree of Master of Science (Communications
Engineering)
…………………………………..
Md. Noor B. Salleh
Dean, Kulliyyah of Engineering
v
DECLARATION
I hereby declare that this dissertation is the result of my own investigation, except
where otherwise stated. I also declare that it has not been previously or concurrently
submitted as a whole for any other degrees at IIUM or other institutions.
Haytham I. M. Alzeini
Signature…………………. Date …..................
vi
INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA
DECLARATION OF COPYRIGHT AND AFFIRMATION
OF FAIR USE OF UNPUBLISHED RESEARCH
Copyright ©2014 by International Islamic University Malaysia. All rights reserved.
HYBRID SPECTRUM SENSING USING ENERGY DETECTOR
AND CYCLOSTATIONARY FEATURE DETECTION WITH
WIRELESS DISTRIBUTED COMPUTING CONCEPT
No part of this unpublished research may be reproduced, stored in a retrieval system,
or transmitted, in any form or by any means, electronic, mechanical, photocopying,
recording or otherwise without prior written permission of the copyright holder except
as provided below.
1. Any material contained in or derived from this unpublished research may
be used by others in their writing with due acknowledgement.
2. IIUM or its library will have the right to make and transmit copies (print
or electronic) for institutional and academic purposes.
3. The IIUM library will have the right to make, store in a retrieval system
and supply copies of this unpublished research if requested by other
universities and research libraries.
Affirmed by Haytham I. M. Alzeini
……..……..…………… …………………..
Signature Date
vii
ACKNOWLEDGEMENTS
This work would not have been possible without the support and encouragement of
my supervisor Dr. Shihab A. Hameed and my Co-supervisor Dr. Mohamed H.
Habaebi. I would like to thank them both for guiding and helping me throughout the
research from the early beginning to the end.
I would like to thank all my teachers for all knowledge they provided during the
course work period. I emphasize that such a knowledge has added a highly valuable
touch to my research. Special thanks for Dr. Aisha Hassan Abdalla who enlighten the
way of research for me.
I cannot end without thanking my amazing parents, Mr. Ibrahim Alzeini and Mrs.
Zubaida Alkurd who taught the alphabet and counting from one to ten. Every
countable knowledge I have obtained ever since is just built upon that. Finally, my
thanks to my beloved Sharon Jones, whose opinions were always helpful, encouraging
and supporting for the past eight months of hardworking.
viii
TABLE OF CONTENTS
Abstract .......................................................................................................................... ii Abstract in Arabic ......................................................................................................... iii Approval page ............................................................................................................... iv Declaration ..................................................................................................................... v
Copyright Page .............................................................................................................. vi Acknowledgement ....................................................................................................... vii
List of Tables ................................................................................................................ xi
List of Figures .............................................................................................................. xii List of Abbreviations ................................................................................................... xv
CHAPTER ONE: INTRODUCTION ........................................................................ 1
1.1 OLAP Technology ........................................................................................ 1
1.1.1 OLAP History .................................................................................... 2
1.1.2 OLAP vs. OLTP ................................................................................. 4
1.2 Problem Statement and Motivation ............................................................ 10
1.3 Research Scope ........................................................................................... 11
1.4 Study Objectives ......................................................................................... 11
1.5 Research Methodology ............................................................................... 12
1.6 Contributions .............................................................................................. 14
1.7 Thesis Breakdown ...................................................................................... 14
CHAPTER TWO: LITERATURE REVIEW ......................................................... 16
2.1 Overview..................................................................................................... 16
2.2 Materialization ............................................................................................ 16 2.2.1 Cube and Sub-Cube Construction .................................................... 17
2.2.2 Compression ..................................................................................... 19
2.2.3 View Selection ................................................................................. 19
2.2.4 Distributed OLAP ............................................................................ 20
2.3 Real-Time OLAP ........................................................................................ 26
2.3.1 Multi-core OLAP Processing ........................................................... 27
2.3.2 GPU OLAP Processing .................................................................... 28
2.3.3 Heterogeneous OLAP Processin ...................................................... 28
2.3.3.1 General Performance Enhancement ...................................... 29
2.3.3.2 OLAP Cube Creation ............................................................ 31
2.3.3.3 OLAP Queries Improvement ................................................. 32
2.3.3.4 OLAP Memory Ameliorating ................................................ 33
2.3.4 Sequential OLAP Processing ........................................................... 33
2.3.4.1 Intensive Computing ............................................................. 34
2.3.4.2 Multidimensional Complex Patterns ..................................... 35
2.4 OLAP Stage of the Art Summary ............................................................... 36
2.5 String Search Algorithms ........................................................................... 39 2.6 Aho-Corasick Algorithm ............................................................................ 40
2.6.1 Aho-Corasick Time Complexity ...................................................... 40
ix
2.7 Boyer-Moor Algorithm ............................................................................... 41 2.7.1 Shift Rules ........................................................................................ 41
2.7.1.1 Bad Character Shifting Rule .................................................. 42
2.7.1..2 Good Suffic Shifting Rule .................................................... 42
2.7.2 Boyer-Moore Time Complexity ....................................................... 44
2.8 Knuth-Morris-Pratt Algorithm ................................................................... 44 2.8.1 Knuth-Morris-Pratt Time Complexity ............................................. 46
2.9 Rabin-Karp Agorithm ................................................................................. 47
2.9.1 Rabin-Karp Time Complexity.......................................................... 47
2.10 Summary ................................................................................................... 47 2.10.1 Rabin-Karp Justification ................................................................ 47
CHAPTER THREE: OLAP SERVER SCHEMA DESIGN ................................. 53 3.1 Overview..................................................................................................... 53
3.2 Distributed OLAP System (high level design and workflow) .................... 54 3.2.1 High Level Schema Design .............................................................. 55
3.2.2 High Level Workflow ...................................................................... 57 3.3 Heterogeneous OLAP System (low level design and workflow) ............... 58
3.3.1 Rabin-Karp Algorithm ..................................................................... 60 3.3.2 Optimized Rabin-Karp Algorithm ................................................... 61
3.3.3 Low Level Workflow ....................................................................... 65
3.3.3.1 CPU Tasks Workflow ............................................................ 66
3.3.3.2 GPU Tasks Workflow ........................................................... 68
3.3.3.3 Upper and Lower Threshohlds .............................................. 68
3.4 Summary ..................................................................................................... 69
CHAPTER FOUR: IMMPLEMENTATION AND RESULTS ............................ 71 4.1 Overview..................................................................................................... 71 4.2 Experimental Set up .................................................................................... 71
4.2.1 Hardware Specifications .................................................................. 72
4.2.1.1 Sony VPCSB36FG ................................................................ 72
4.2.1.2 Dell XPS 8700 ....................................................................... 73
4.2.2 Software Specifications .................................................................... 73 4.2.3 The Code .......................................................................................... 74
4.3 Experiments Flowchart ............................................................................... 74 4.4 Empirical Results ........................................................................................ 75 4.5 Upper and Lower Thresholds ..................................................................... 83
4.5 Summary ..................................................................................................... 85
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS .................... 88 5.1 Conclusion .................................................................................................. 88
5.2 Future Work ................................................................................................ 90 5.2.1 Sequential OLAP ............................................................................. 91
5.2.2 OLAP Physical Data Structure......................................................... 91 5.2.3 OLAP and Big Data ......................................................................... 91 5.2.4 ETL Architecture for Real-Time OLAP and Streaming Data ......... 92
x
REFERENCES ........................................................................................................... 93
APPENDIX A: THE CODE ...................................................................................... 101 APPENDIX B: GLOSSARY ..................................................................................... 111 APPENDIX C: LIST OF PUBLICATIONS .............................................................. 112
xi
LIST OF TABLES
Table No. Page No.
1.1 Sample sales spreadsheet
6
1.2 Comparison between OLTP and OLAP systems
9
2.1 State of the art conclusion and criticism
36
2.2 Algorithms comparison from OLAP point of view
49
xii
LIST OF FIGURES
Figure No. Page No.
1.1 OLAP and OLTP interaction 4
1.2 OLAP operations 5
1.3 Sample cube 7
1.4 Three-dimensional cube 8
1.5 Four-dimensional cube 8
1.6 Four-dimensional cube 8
1.7 Building four-dimensional cube
8
1.8 Five-dimensional cube
9
1.9 Research Methodology
13
2.1 Bad Character Shifting match (Boyer-Moore)
42
2.2 Bad Character Shifting Mismatches (Boyer-Moore) 43
2.3 Good Suffix Shifting Matches (Boyer-Moore) 43
2.4 Good Suffix Mismatches (Boyer-Moore) 43
2.5 Boyer-Moore rules example
44
2.6 KMP Algorithm’s First Shift
45
2.7 KMP Algorithm’s Second Shift
45
2.8 KMP Algorithm First Match
46
2.9 KMP Algorithm Second Match
46
3.1 Distributed Enterprise
55
3.2 Distributed OLAP Schema 56
xiii
3.3 Frontend and Backend OLAP Servers Schema 59
3.4 Rabin-Karp Algorithm 60
3.5 Pseudo-code for Optimized Rabin-Karp Algorithm
62
3.6 Quad-Core Intel CPU
63
3.7 Simplified AMD GPU architecture
63
3.8 Interaction between GPU and CPU
64
3.9 Optimized Rabin-Karp algorithm workflow
67
4.1 Response Time Calculation Code 74
4.2 Experiments Flowchart 75
4.3 Phase (1): 4KB patterns recognition time – VPCSB 77
4.4 Phase (2): 8KB patterns recognition time – VPCSB 78
4.5 Phase (3): 16KB patterns recognition time – VPCSB 79
4.6 Phase (1): 4KB patterns recognition time – XPS 8700 80
4.7 Phase (2): 8KB patterns recognition time – XPS 8700 81
4.8 Phase (3): 16KB patterns recognition time – XPS 8700 81
4.9 Threshold β and the comparative gain 84
4.10 Proportional gain of Heterogeneous Rabin-Karp algorithm –
VPCSB
86
4.11 Proportional gain of Heterogeneous Rabin-Karp algorithm – XPS
8700
86
4.12 Achieved Gain Using Heterogeneous RK Algorithm Comparison 87
xiv
LIST OF ABBREVIATIONS
BAA. Blending-As-Aggregation
BIA. Business Intelligence Accelerator.
CPU. Central Processing Unit
CUDA. Computer Unified Device Architecture
DB. Database.
DFA. Deterministic Finite Automaton
DPA. Data Placement Advisor.
DSS. Decision Support System
DW. Data Warehouse
ETL. Extract, Transform, and Load Process
FAR. Fragment Aggregation and Recombination
FSM. Finite State Machine
GA. Genetic Algorithm
GFS. Google Files System
GPU. Graphical Processing Unit
I3DC. Interactive Three-Dimensional Cubes
MPI. Message Passing Interface
MQT. Materialized Query Tables
OLAP. Online Analytical Processing
OLTP. Online Transactional Processing
RDBM. Relational Database Management Systems
BAA. Blending-As-Aggregation
BIA. Business Intelligence Accelerator.
CPU. Central Processing Unit
CUDA. Computer Unified Device Architecture
DB. Database.
DFA. Deterministic Finite Automaton
DPA. Data Placement Advisor.
1
CHAPTER ONE
INTRODUCTION
1.1 OLAP TECHNOLOGY
Online analytical processing (OLAP) by definition is a category of software
technology that enables analysts, managers and executives to gain insight into data
through fast, consistent, interactive access to a wide variety of possible views of
information that has been transformed from raw data to reflect the real dimensionality
of the enterprise as understood by the user. OLAP functionality is characterized by
dynamic multidimensional analysis of consolidated enterprise data supporting end
user analytical and navigational activities including calculations and modeling
applied across dimensions, through hierarchies and/or across members, trend
analysis over sequential time periods, slicing subsets for on-screen viewing, drill-
down to deeper levels of consolidation, rotation to new dimensional comparisons in
the viewing area …etc (OLAP council 2013). OLAP is offering a new approach of
storing dimensional relational database. In which, the approach suggests storing data
in ‘multi-dimensional cubes’ instead of the traditional tables. This gives an advantage
of visualizing data in multi-dimensional manner so data can be seen from different
points of view. In addition, applying four main operations on these cubes (slice, dice,
drill down, roll up) which will be elaborated in the following sections.
OLAP heals several issues in IT fields and offers solutions to many difficulties
are being encountered. These difficulties included the slow query results for online
transactions and the lack of data projection flexibility. The major goal was to reduce
2
the on-fly processing processes by preprocessing every possible query. This allows the
data to show instantaneously whenever the user run one of these queries. In fact, what
enhances this technology is the ability of presenting statistical information, identifying
unusual patterns and exploring trends by analyzing multidimensional cubes of data
interactively. However, due to the high complexity of data structures of business and
accounting sector; digging out to identify complex queries has not been an easy
mission for both IT specialists and ordinary users as well (Chen, Dehne, Eavis, 2008).
Querying becomes even harder when digging down up to more than three or four
levels of related tables.
Nonetheless, OLAP technology has been considered a breakthrough in data
mining field whereby OLAP located under this category of IT along with data mining
and relational database and so forth. Typical OLAP applications includes, but limited
to, reporting, process management, budgeting, market and weather forecasting,
medical applications and intelligence applications in business, finance, healthcare and
military fields. However, we can argue that most, if not all, OLAP application serves
one goal, decision support utilized by decision support systems (DSS) which
encompasses wide range of systems and application that belong to the aforementioned
IT categories (Chen, Dehne, Eavis, 2008).
1.1.1. OLAP History
The seeds of OLAP idea can be seen in early 1960’s when Kenneth Iverson had
introduced the first multidimensional programming language APL (A Programming
language) which offered processing operators and multidimensional variables.
However, due to hardware resource requirements, APL market had declined
3
significantly even though some of its ideas still surface in few modern OLAP tools.
Despite the fact that the term OLAP has come to the world in the mid of 90’s (Gray,
Chaudhuri, Bosworth, Layman, Reichart, Venkatrao, Pellow, Pirahesh, 1997) (1993 to
be specific by the father of the relational database Edgar F. Codd); the first
commercial tool usage can be traced back to the 1970’s when the first market related
product (Express) had been released. Indeed, Oracle9i OLAP by ORACLE is one of
the main Express’ successors. Essbase was the first commercial OLAP product in the
1990’s that has been used under new term usage; the product had been followed by
many products with strong growth in late 90s. SSAS by Microsoft was one of the
main products that had been released in 1998.
By enabling users to smoothly and dynamically manipulate transactional data
in relational database with real multidimensional environments, PowerOLAP had been
considered as big jump had been achieved in 1997 by PARIS Technologies. The new
product has been described as a milestone in OLAP evolution life cycle and a
revolutionary event as it has been utilized by Excel and Web to allow users connecting
in an organization. OLAP@Work Excel Add-In allows users to take full advantage of
OLAP services with all features. In 2004, this feature had gone to the mainstream and
many vendors have launched their own versions concurrently.
Today, OLAP has a wide range of applications in numerous industries.
Regardless the commercial version that is being utilized, the idea of OLAP is still
dominating in data mining field.
4
1.1.2. OLAP vs. OLTP
OLTP (On-line Transaction Processing) is described by a huge number of short on-
line transactions. That includes INSERT, DELET, UPDATE processes which are used
on daily basis to facilitate business applications mostly (Seagat series 2002). OLAP,
on the other hand, is described by relatively low, but complex, number of transactional
queries which usually involve multidimensional aggregations. OLAP is used for long-
term business plans and support decisions making. Figure 1 shows the interaction
process between the two technologies.
Figure 1.1: OLAP and OLTP interaction
On contrast to OLTP – which is integrated with databases; OLAP has a
completely different data structure that is based on cubes rather than traditional tables.
This approach of storing data (cubes) offers an essential advantage of analyzing data
and deliver conclusions by applying a certain cube operations Figure 2 show these
operations which includes drill-down, slice, dice roll-up. Although this mechanism
causes a delay due to the enormous amount of data and cube processing operations;
5
OLAP has an advantage over OLTP in terms of processing results. In order to present
the OLAP vs. OLTP issue clearly; the drawbacks of OLTP in front of OLAP manifest
themselves into the following points (Gray et al, 1997):
i. Limited data storage: retrieving tens of thousands of rows in order to
analysis and present them in short time is a challenge, particularly when
users use the same application at the same time and if each user is
retrieving completely different rows.
ii. Data versus Information: answers are demanded by business industries, an
OLAP application would always give answers based on few-seconds
response analysis, users need data accompanied with meta data,
information.
iii. Data layout: the way which OLAP applications use to store data is to serve
big numbers of questions in parallel. This need to work with aggregated
data in order to answer high-level questions. In other words, to overcome
the drawback of OLTP we better use different techniques rather than waste
more money on bigger and faster databases.
Figure 1.2: OLAP operations
6
All OLAP tools use the same high-level concept to present data, that is:
multidimensional cube, since cubes are easy to imagine and capture in minds. Cubes
are much different from traditional databases, the cube is a conceptual design and at
the same time is a logical design. In order to understand the cube, we apt to compare it
with a database table. Table 1.1 (Pedersen, Jensen, Dyreson, 2001) is a useful tool to
analyze sales data. A pivot table is a two-dimensional spreadsheet with associated
subtotals and totals which enhances complex views to be easier to understand though
it is still a two-dimensional axis. Hence, traditional tables – spreadsheet – are not
sufficient to manage and store multidimensional data since they cannot segregate the
real information – structural view – from the desired view of information.
Multidimensional databases view data as cubes generalize spreadsheets to any number
of dimensions (Pedersen et al, 2001).
Table 1.1 Sample sales spreadsheet
Figure 1.3 shows the same information in the table 1.1 but in cube technique,
in general, a cube enhances viewing two or three dimensions simultaneously;
however, it can show up to four low-cardinality dimensions using nesting, it further
decreases at query time by projecting the data down to 2D or 3D by aggregating the
values, e.g. in order to view sales by city and time.
7
Figure 1.3: Sample cube
Despite the fact that it implies three dimensions; the acronym cube can
theoretically have any number of dimensions. Indeed, most real-world cubes have four
and more dimensions.
Although attempting to imagine a multidimensional cube can be somehow
tough; the understanding of its advantages could help. Now try to visualize that we
have few three-dimensional cubes, each of which has the same structure and we need
another measure, let us assume the day’s trading. Then we need to merge them. To do
so we create a fourth dimension. It is not easy to draw such a cube; however, it is not
difficult to realize the integrity of the design. Most of the literature suggests that we
can just retrieve and work with such cube simply without the need to draw or imagine
the entire cube since most applications present only a two-dimensional view of the
data even when the actual cube is more that three-dimensional cube. Here, few figures
are sufficient to realize how multidimensional cube that can be imagined and drawn.
Figure 4 shows the three-dimensional cube while figures 5, 6 show the same
information with the additional fourth dimension in two ways respectively (Seagat
series 2002). Figures 7 and 8 (Hybercube geometry, 2013) depict the
multidimensional cubes construction in spatial manner.
8
Figure 1.6: Four-dimensional cube
Figure 1.4: Three-dimensional cube Figure 1.5: Four-dimensional cube
Figure 1.7: Building four-dimensional cube
9
Finally, we summarize the differences between OLAP and OLTP by the
following table 1.2 (Mauve, Fuessler, Widmer, Lang, 2011).
Table 1.2: Comparison between OLTP and OLAP systems
Feature OLTP OLAP
Characteristic operational processing informational processing
Orientation transaction analysis
User clerk, DBA, database
professional
manager, executive, analyst
Function data-to-day operations long term informational requirements,
decision support
DB design E-R based, application- star/snowflake, subject-oriented
Figure 1.8: Five-dimensional cube
10
oriented
Data current historical
Summarization primitive, highly detailed summarized, consolidated
View flat relational multidimensional
Unit of work short, simple transaction complex query
Access read/write mostly read
Focus data in information out
DB size 100 MB to GB 100 GB to TB
Priority high performance, availability high flexibility
Metric transaction throughput query throughput, response time
1.2 PROBLEM STATEMENT AND MOTIVATION
Generally, OLAP works with data warehouses with data size of Gigabytes to
petabytes (1015 Byte) in certain instances. Therefore, analyzing and processing such
size requires powerful processing capabilities. Moreover, most of OLAP applications
are time sensitive and critical (medical and finance applications) which entails the
need of very short response time. Having said that, processors, in particular cases;
have to handle billions of rows – reading and processing – in few seconds or less.
Many enhancements have been proposed and these enhancements have stemmed from
different diagnoses, mainly can be divided into two main streams: materialization-
oriented solutions and hardware-oriented solutions. It has been elaborated why
materialization does not meet all our optimized criteria (especially Real-Time
requirement) (Alzeini, Hameed, Habebi, 2013). Thus, the problem is how to achieve
Real-Time OLAP application using the existing resources, and optimally taking
