=Paper=
{{Paper
|id=Vol-1580/41
|storemode=property
|title=Using Big Data Classification and Mining for the Decision-making 2.0 Process
|pdfUrl=https://ceur-ws.org/Vol-1580/id41.pdf
|volume=Vol-1580
|authors=Rhizlane Seltani,Noura Aknin,Souad Amjad,Kamal Eddine El Kadiri
|dblpUrl=https://dblp.org/rec/conf/bdca/SeltaniAAK15
}}
==Using Big Data Classification and Mining for the Decision-making 2.0 Process==
https://ceur-ws.org/Vol-1580/id41.pdf
Proceedings of the International Conference on Big Data Cloud and Applications
Tetuan, Morocco, May 25 - 26, 2015
Using Big Data Classification and Mining for the
Decision-making 2.0 Process
Rhizlane Seltani1,2 Noura Aknin1,2
1,2 1,2
Information Technology and Modeling Systems Research Information Technology and Modeling Systems Research
Unit, LIROSA Laboratory Unit, LIROSA Laboratory
Faculty of Science, Abdelmalek Essaadi University Faculty of Science, Abdelmalek Essaadi University
Tetuan, Morocco Tetuan, Morocco
sel.rhizlane@gmail.com aknin@ieee.org
Souad Amjad1,2 Kamal Eddine El Kadiri2
1,2 2
Information Technology and Modeling Systems Research Computer Science, Operational Research and Applied
Unit, LIROSA Laboratory Statistics Laboratory
Faculty of Science, Abdelmalek Essaadi University Faculty of Science, Abdelmalek Essaadi University
Tetuan, Morocco Tetuan, Morocco
amjad_souad@uae.ma elkadiri@uae.ma
Abstract—Web 2.0 is a revolution that has affected all decision-making systems, to generate more meaningful and
areas, especially those of the new technology. Several new relevant decisions.
concepts have emerged, and a large number of innovative
applications continue to come out every day. However, the To classify and process data, various algorithms and
social networking remains the racehorse of web 2.0, giving the techniques can be used. These methods differ depending on
user at the same time, a space for communication and for data types. In the case of big data, to retrieve information,
information sharing, which generates too much data, variable there are various analysis techniques with different
and characterized by a great creation speed. So, we can call orientations and results, such as Representation-learning
them big data, and consider them a very rich and interesting methods based geometric information, Stream Classification
basis for decision-making. Algorithms, Associative Classifiers, etc.
Big Data is a type of data which are characterized by the
veracity, important volumes, and increasing variety and In this paper, we discuss some methods that we can use
velocity, which makes their treatment and their processing by to classify big data in order to elaborate decisions, report the
traditional database management tools a very difficult task. To strengths and the weaknesses. And therefore, present our
overcome this problem, we opt for the big data classification global framework of decision-making 2.0 based on big data
process. classification by describing the key pillars to be considered,
In this paper, we make a study of some big data to lead well the classification process for the purpose of
classification methods, which are the most significant to be decision-making.
used to classify big data dedicated to decision-making, we
detect their points of strength and weakness. Then we propose II. WEB 2.0
a framework summarizing the process of the formulation of
the decision from the web 2.0 content, based on the big data A. Definition
classification, and we specify the criteria to be taken into
account when choosing the big data classification methods The web 2.0 is a combination of technologies, business
intended for the decision-making. plans and social skills, which allow users to create web
content, and to be more involved in the process of the
Keywords—Web 2.0; Big Data; Decision-making; Data management of this content. It has brought many creative
Classification concepts and techniques that did not exist before and which
made the electronic life simpler and more enjoyable [1][2].
I. INTRODUCTION With the web 2.0, a new era of web use is born. Several
applications have been developed and which have enriched
The large variety of applications that appeared after the our lives by allowing more of interactivity and collaboration,
emergence of the web 2.0, produce a huge mass of various such as blogs and social networks [3].
and diverse data. This wealth of information is a very
important resource that we want to exploit to enrich our
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B. Architecture and Principals Data Volume: refers to a very important quantity of
Web 2.0 is based on a varied and robust architecture, generated information. Data is considered as big
founded on the introduction of new principles such as data if their size is very large, so we cannot control
collaboration and interactivity, and the use of new them to make analysis easily.
applications like web interface design techniques, those of
Data Variety: This makes analyzing this type of
content syndication, XHTML, URL, etc [4].
data a very difficult mission. We have more
There are several emerging principles with the different data presentation formats: text, audio,
appearance of web 2.0, the most notable: image, etc.
Collaboration: This is an important aspect which Data Velocity: It refers to the speed of creation and
describes when a user has the opportunity to generation of data, which have been increased with
contribute in the creation of the web content by the different new web applications.
creating its own content.
Data Veracity: Data veracity refers to the
Interactivity: one of the introduced principles by the anomalies in data. Veracity in data analysis
web 2.0, interactivity is reflected by the interaction constitutes the biggest challenge to overcome,
of the user with the web content and with other because, veracity of data sources can largely affect
users. the precision of analyzes.
These two principles constitute new trends that have
changed our lives and our way of working, they are the basis IV. BIG DATA CLASSIFICATION FOR DECISION-MAKING
of social networks, blogs, wikis, etc.
A. Clustering
III. BIG DATA Clustering (also called Cluster Analysis), is a task of data
mining, which means the mission of assembling a set of
A. Definition objects, by the way that, objects which belong to the same
The term big data refers to data sets exchanged by group have more similarities than with those belonging to
connected objects in the web, and whose volumes are others groups. A group is called a cluster. The clustering was
important and the variety and the velocity are increased [5]. used for the first time in the classification tasks by Cattell in
It is a compilation of data sets which are characterized by 1943 for personality psychology classification [7]. Many
complexity and large volume, so their management and clustering algorithms exist. Making the choice about which
processing constitute a difficult task if we use traditional algorithm we must use, depends on the used cluster models
[8]. Among the most distinctive cluster models, we find:
database management tools [6].
Centroid models, Distribution models, Group models, and
Connectivity models.
B. Characteristics
Compared to other types of data, big data are different In addition to its important role in the classification task,
and have some specifications. These differences concern clustering has several advantages, such as the definition of
several facets as the data format, their volume, the time information relating to the data, which were not revealed
required for their creation, and their nature. before, as associations, so we can look for new patterns.
Also, clustering provides a logical structure which makes
The principal features are: Data volume, data velocity, results read and interpreted easily. But it is not the case, if we
data variety, and data veracity. We can consider these opt for a large scale of clusters, because there are no
elements as the characterizing pillars of big data (Fig. 1.), definitive methods to determine precisely the suitable
and which make their processing and their analysis a special number of clusters.
challenge.
B. Decision Trees
The decision tree is a technique which we can use for
classification tasks, by creating a model to predict the output
value based on a number of input values [9] [10]. To use
decision trees for classification, we construct trees starting
by the root of the tree, and subsequently, proceeding down
to its leaves.
A classification rule is developed based on example
objects, which are known by their values of a collection of
attributes. Then, the decision tree is expressed in function of
the same attributes [11]. Decision trees constitute a good
Fig. 1. Characterizing Pillars of Big Data way to well represent decisions. An example of a decision
tree form is shown in the Fig. 2.
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Classification by Emerging Patterns: based on
emerging patterns from a sample, which means
event associations whose supports vary, depending
on the dataset [21].
Classification based on High-Order Pattern: is a
classification system, which uses the algorithm of
high-order pattern discovery, which detects
considerable connection or association patterns
using residual analysis in statistics [22].
Associative Classifiers based on the Apriori
Algorithm: the Apriori Algorithm is an algorithm
which proceeds by determining the prevalent items
in the database. So, we can define association
rules to wrap up trends in the database, many
applications in various domains were done using
this technique, such as market basket analysis [23].
Associative classification provides a high accuracy and
Fig. 2. A General Form of a Decision Tree
it is easy to understand. However, it presents some
challenges, like the lack of obvious criteria to classify
The decision trees are characterized by the robustness
objects. Since it is based on a large number of rules, the
and the simplicity of understanding and interpreting. What
process of its elaboration is a time-consuming task, and it
is important about decision trees is that we can treat
becomes a difficult task to select the suitable ones to
categorical and numerical data. On the other hand, decision
develop the classifier.
trees are instable, since a miniature change in the input data
can affect the entire tree, by causing large changes in it.
V. BIG DATA CLASSIFICATION AS A BASIS OF DECISION-
C. Support Vector Machines MAKING 2.0
Support vector machines, more usually SVMs, were A. The Data Generation Process
introduced the first time for binary classification. They refer
to a collection of methods used for regression Web 2.0 is a very important source of information. The
and classification, to analyze data in order to verify to which user interacts continuously with the web content through
category an element belongs [12]. They can be used in collaborative applications, such as blogs, social networks,
several ways depending on the nature of their application, etc. With the increase of the number of actors on the web,
such as, text categorization, recognition of images, hand- the rate of information circulating on its channels increases.
writing code, bioinformatics, etc. This large data flow generates the phenomenon of big data.
Hence, web 2.0 is a rich platform of information, which can
Some of the advantages of using SVM algorithms are:
be treated to generate significant data. The user is primarily
the robustness, the ability to learn well using a few
parameters, and the computational efficiency. On the other a passive actor, becomes in an instant an active actor, by
hand, apply SVM can at times require taking into transmitting opinions, which we propose to treat to ensure
consideration many aspects of learning methods [13], SVM the mission of decision-making. These opinions can take,
is oriented to be applicable directly in the case of two-class for example, the form of:
tasks. For that reason, when we deal with a multi-class task, A solution to a particular problem: a problem can be
we must use algorithms that can reduce it to a set of binary
solved quickly and efficiently if the process of the
problems, or take account of all the classes at once by giving
generation of the solution is collaborative. So the
one formulation of optimization for all the data. Different
methods of treating multi-class support vector machines reviews, including those of experts, about an issue
continue to emerge [14]. may be of great use to make decisions to solve a
given problem.
D. Associative Classification A feedback to a given subject: any feedback
Associative classification refers to a classification which contains in itself a notice that we can use to extract
is based on the use of association rules, by combining both useful information which enriches the process of the
classification and mining of associations [15] [16]. decision making.
Compared to other approaches, it is considered a highly
A proposal for improvement: in any field,
accurate and competitive method, and can be applied in
application, or system, we always look for ways of
different ways [17] [18] [19] [20]. We can define three types
improvement, especially in the case of business.
of associative classification systems:
Opinions of clients and in particular those which are
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the most affected by the service, constitute a very In the decision-making 2.0 process, the classification
important resource of inspiration to make the right serves as a passage from the raw data to the classified ones,
decision of improvement. which will be used later to generate decisions. Data which
circulate across the web, especially in social networks,
A complaint about a process, a product, a service: as blogs, etc, are difficult to track and manage. So to overcome
with proposals for improvement, complaints also this problem, our classification process should follow some
lead to the generation of significant decisions about specifications to properly carry out this mission.
a product, a process, a service, etc.
Taking into consideration our aim, which is decision-
B. Decision-Making 2.0 Based Big Data Classification making based on the content reflected by the comments and
Model the feedbacks of users, and to provide relevant decision,
To exploit the generated data on the web 2.0, it is which must be generated based on meaningful data, our
necessary to isolate the significant information. Circulating classification process must be efficient and suits our
data through the web 2.0 applications such as social purpose.
networks have the characteristics that make them a part of As already mentioned, the classification methods have
what is called big data. To process them, we proposed to drawbacks as advantages. That is why, we opt for a
adopt a classification process. combination, to elaborate a multiple classification model to
When we want to treat data based on the web 2.0 exploit the strengths of the cited methods, taking into
content, in order to make decisions. A simple comment or account different parameters, as shown in the Fig. 4.
tweet can generate a large data stream, through feedbacks of Accuracy: the classification process must guarantee
users. Taking account of these data in decision-making is high accuracy, to ensure the relevance of our
very important to harness the collective intelligence. decisions, which is a very important factor for the
After a preliminary process of data streams, to centralize evaluation of the quality of the decision.
those that meet our study needs, comes the classification Facility of understanding: it is essential that
phase to derive classified data according to specific classification must be a process that provides results
parameters that depend on the issue in question. Finally, we which are easy to understand. It means also, that
get the basis of decision-making. The framework which results must be interpreted without difficulties.
presents the general process starting with the creation of the
data on the web and ending with the decision-making is Flexibility: flexibility is represented by the fact that
represented in the Fig. 3. the classification can take into consideration
categorical data, and not just the numerical ones, for
more significant and common decisions.
Fig. 4. Pillars of Big Data Classification for a Decision-making 2.0
Process Model
Fig. 3. Process of the Generation of the Decision 2.0 Based on the Big
Data Classification
32
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ACKNOWLEDGMENT [22] Y. Wang, High-order pattern discovery and analysis of discrete-
The authors of this paper would like to thank our valued data sets. PhD thesis, University of Waterloo, Waterloo,
Ontario, Canada, 1997.
Research Team, Information Technology and Modeling [23] R. Agrawal, and R. Srikant, “Fast algorithms for mining association
Systems Research Unit, and more generally, the Computer rules.” In Proc. 20th int. conf. very large data bases, VLDB (Vol.
Science, Operational Research and Applied Statistics 1215, pp. 487-499, 1994.
Laboratory, from the Faculty of Science, Abdelmalek
Essaadi University of Tetuan, Morocco, for their great
support.
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