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Procedia Computer Science 151 (2019) 636–642

1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs. 10.1016/j.procs.2019.04.085

10.1016/j.procs.2019.04.085 1877-0509

Available online at www.sciencedirect.com

Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia

The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40) April 29 - May 2, 2019, Leuven, Belgium

A New Model for Integrating Big Data into Phases of Decision-Making Process

Fatma Chiheba,∗, Fatima Boumahdia, Hafida Bouarfaa aLRDSI Laboratory, sciences faculty, Saad Dahlab University, BP 270 Soumaa road, 09000 blida, Algrie

Abstract

Big data is becoming a key factor within organizations since the application of Big Data in modern business provides information in real-time that allows organizations to take a faster and smarter decision. Therefore, big data is widely used in many domains of modern society to achieve progress in these domains. This study aims to harness the opportunity of Big Data to enhance the decision-making process in the organization. The objective of this paper is to develop a theoretical model that integrates big data into the decision-making process to improve the decision-making process in the organization. The proposed model relies on three basic elements, namely: (1) decision-making process that includes four phases: intelligence, design, choice, and implementation phase;(2) big data analytics to collect, store, manage, and analyze the huge amount of diverse data to extract business value; and (3) decision-modeling using Decision Model and Notation standard to support the communication between the decision makers and Big Data analytical team during the phases of the decision-making process. The interaction between these three elements enhances the decision-making process to make smarter and faster driven-data decisions that make a real impact in the organization.

c© 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.

Keywords: big data; decision-making; decision-making process; decision-modeling; Decision Model and Notation (DMN) standard.

1. Introduction

Nowadays, we live in the technology age where a huge amount of digital data is generated exponentially at an unprecedented speed. This evolution of data is accompanied by an advance in technologies that enable organizations to collect, store, manage and analyze such data to transform it into information and knowledge. This phenomenon is known as Big Data.

The term Big Data associated in the most of extant definitions in the literature with the notion 3V (volume, variety and velocity) [26], while other definitions include other Vs such as Value [6] and veracity [8, 25]. Drawing on studying many extant Big Data definition, the study [4] argued that the term Big data describes the high Volume, Velocity and

∗ Corresponding author. Tel.: +213-0696986184. E-mail address: [email protected]

1877-0509 c© 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.

Available online at www.sciencedirect.com

Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia

The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40) April 29 - May 2, 2019, Leuven, Belgium

A New Model for Integrating Big Data into Phases of Decision-Making Process

Fatma Chiheba,∗, Fatima Boumahdia, Hafida Bouarfaa aLRDSI Laboratory, sciences faculty, Saad Dahlab University, BP 270 Soumaa road, 09000 blida, Algrie

Abstract

Keywords: big data; decision-making; decision-making process; decision-modeling; Decision Model and Notation (DMN) standard.

1. Introduction

∗ Corresponding author. Tel.: +213-0696986184. E-mail address: [email protected]

2 Author name / Procedia Computer Science 00 (2018) 000–000

Variety of data that require specific Technology and Analytical Methods for its transformation into Value. Volume describes the high size of data generated every day from mobile devices, social media, Internet of Things (IoT), and so on. Velocity defines the high rapidity of data production and the speed required for data processing and analyzing. Variety considers the diversity of data type created that includes structured and unstructured data such as video, images, text etc. while Value describes the business insight that could be extracted through big data analytics.

Big data is successfully used in many sectors of modern society to derive business insight from the data generated by the daily activity of each sector and other sources of data. For example, the study [17] exploited emergency department data, Twitter data, Google search data, and environmental sensor data to predict the potential number of asthmatics who will visit the emergency department in a particular area; the study [27] succeeded in identifying the high potentials luxury car buyers using car owners and telecom users data; the study [13] was able to predict trends in the Indian stock market daily and monthly through processing news, social media data, and historical price; the study [2] used Twitter data to classify twitter users according to their political orientation( Democrats or as Republicans) based on the political content in tweets; and the study [19] examined authenticity and sentiment polarity towards the brand ”Starbucks” through the analysis of social media data collected from Twitter; and so on.

The insights extracted from Big Data have the potential to help organization making smarter and faster decisions that make a real difference in these sectors. However, the business insight cannot be extracted automatically of me- chanically applying the tools of big data to the data, instead, they are created from the collaboration between analysts and business managers in existing decision-making structures and processes using data and analysis tools to discover new knowledge [18]. Therefore, organizations need to adapt their organizational decision-making process to leverage the opportunity of Big Data [18]. In this context, this study aims to address this need by integrating Big Data into the decision-making process of the organization to enable making faster and smarter decision-driven data.

Our study proposes a theoretical model that combines decision-modeling and Big Data analytics during the phases of the decision-making process. Our proposed model is called DMP-BDE Model: Decision-Making Process in a Big Data Environment Model. DMP-PDE Model bases on three main elements: (1) decision-making process proposed by Simon [20] that includes four phases: intelligence, design, choice and implementation phase, (2) Big Data analytics, and (3) Decision modeling using Decision Model and Notation (DMN) standard.

The rest of this paper is organized as follows to describe our proposition. Section 2 outlines the background and re- lated works while Section 3 explains the different phases of our model. Section 4 discusses the contributions achieved by this study. Finally, section 5 presents the conclusion of this study.

2. Background and related works

Decision-making is a process of choosing among alternative courses of action in order to attain goals and objectives [9].

Forman and Selly [9] argued that decision-making is the heart of all managerial functions, and that a rich decision- making process is the basis for the success of enterprise because decision-making is absolutely necessary to gain and maintain a competitive advantage.

In literature, we found that there are many decisionmaking models were developed, in order to represent the various phases of the decision-making process. For instance, the study [12] proposed three basic phases to define a decision- making model after studying 25 different strategic decision processes in different organizations; the study [1] defined a model embodied in eight phases; Lunenburg [11] also proposed a rational model that included six steps and other such as [3] and [15].

In our study we chose to use decision model process proposed by Simon [20] because it is the most famous and most referenced in DSS research [10]. This model includes three main phases namely: Intelligence, Design, and Choice phase (the model IDC).

• Intelligent phase: refers to searching the environment for conditions (problem or opportunity) calling for decision. • Design phase: refers to developing and analyzing of alternative solutions to the problem or opportunity. • Choice phase: refers to choosing one or more of the available alternatives developed in the previous phase.

© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.

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Fatma Chiheb et al. / Procedia Computer Science 151 (2019) 636–642 637

Available online at www.sciencedirect.com

Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia

The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40) April 29 - May 2, 2019, Leuven, Belgium

A New Model for Integrating Big Data into Phases of Decision-Making Process

Fatma Chiheba,∗, Fatima Boumahdia, Hafida Bouarfaa aLRDSI Laboratory, sciences faculty, Saad Dahlab University, BP 270 Soumaa road, 09000 blida, Algrie

Abstract

Keywords: big data; decision-making; decision-making process; decision-modeling; Decision Model and Notation (DMN) standard.

1. Introduction

∗ Corresponding author. Tel.: +213-0696986184. E-mail address: [email protected]

Available online at www.sciencedirect.com

Procedia Computer Science 00 (2018) 000–000 www.elsevier.com/locate/procedia

The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40) April 29 - May 2, 2019, Leuven, Belgium

A New Model for Integrating Big Data into Phases of Decision-Making Process

Fatma Chiheba,∗, Fatima Boumahdia, Hafida Bouarfaa aLRDSI Laboratory, sciences faculty, Saad Dahlab University, BP 270 Soumaa road, 09000 blida, Algrie

Abstract

Keywords: big data; decision-making; decision-making process; decision-modeling; Decision Model and Notation (DMN) standard.

1. Introduction

∗ Corresponding author. Tel.: +213-0696986184. E-mail address: [email protected]

2 Author name / Procedia Computer Science 00 (2018) 000–000

2. Background and related works

Decision-making is a process of choosing among alternative courses of action in order to attain goals and objectives [9].

638 Fatma Chiheb et al. / Procedia Computer Science 151 (2019) 636–642 Author name / Procedia Computer Science 00 (2018) 000–000 3

• Simon later added a fourth phase, implementation phase where the chosen solution is put to work , and Mon- itoring can be considered as a fifth phase-a form of feedback [24].

The aim of our study is to help decision-makers taking most advantage of Big Data to solve specific decision problems in the organization by incorporating Big Data analytics into the decision-making process of organization. In literature, we found studies that have already attempted to integrate Big Data analytics during the phases of decision- making process. For instance, the study [16] proposed an integrated model that combines Big Data, Business Intelli- gence and the Decision Support System (DSS) into the mode IDC. They argued that the decision-making process IDC could be supported by the analyses of Big Data using BI to provide valuable information that aid all the phases of the process. While a DSS may be implemented according to the decision problem to predict the most adequate solutions among the alternatives proposed in the choice phase. In another work, Elgendy et al [7] aimed to enhance and support the decision-making process in the organization by integrating the Big Data analytics into the model IDC. The result of their study was the B-DAD framework. The framework integrates the tools, architecture, and analytics of Big Data into the model IDC.

The most important difference between our work and these studies is that our study is interested in using DMN standard with Big Data during the decision-making process to simplify communication between decision-makers and Big Data analytical team during decision-making processs phases. The DMN standard allows modeling human decision in an understandable way. It provides a common notation that is easily understandable by all business users and big data analytical team [14].

The Decision Model and Notation (DMN) standard is a new standard. It is has been developed by OMG to model decisions and their requirements in an understandable way. The decision-modeling using DMN standard includes two levels, decision requirements diagrams (DRD) and decision logic, which could be used independently or in conjunc- tion in a decision model DRD comprises a set of elements and the relationships between them. These elements define the decision that will be made, and how it depends on other decisions, policies or regulation (knowledge source), busi- ness knowledge (knowledge model) and input data . While decision logic specifies the logic used to make individual decisions such as business rules, decision tables, or executable analytic models to allow validation and/or automation of the decision-making processes. [14, 21]

DMN standard could be used for modeling human decision-making and its requirements in an organization using a DRD, modeling the requirements for automated decision-making, or implementing automated decision-making [14].

Decision modeling using DMN standard have been already exploited by [23] to support the Cross-industry stan- dard process for data mining (CRISP-DM) methodology. Taylor [23] explained an analytics project approach based on the CRISP-DM methodology and DMN standard to model decision in the first phase of CRISP-DM methodology. He argued that starting with developing a decision requirements model as part of the Business Understanding phase of CRISP-DM methodology provides data analytical team a better understanding to the decisions. It also helps them identify the data required. Therefore, it provides an obvious business understanding and effective start to the project. In [21] they explained how the new DMN-based approach helped the analytical team to revive projects that were pre- viously insoluble because of the wrong beginnings that led to a misunderstanding of projects objectives. They argued that DMN standard provides a common language between business client and analytics team to talk about decisions rather than the data or analytic techniques to be used. It also allows teams to understand the context surrounding a decision such as the objectives or metrics affected by the decision, input data, knowledge sources, and other decisions are needed to make the decision.

While the paper [22] proposed four iterative steps to develop an effective DRD using DMN standard. These steps are: (1) identify the decisions that are the center of interest of this project, (2) describe decisions: name, short descrip- tion for each decision, and how improving these decisions will influence business, (3) specify decision requirements: specify the requirements of decision (information and knowledge) and combine them into a Decision Requirements Diagram, (4) decompose and refine the model: if a decision need information coming from other decisions, Identify additional decisions needed and describe them and specify their requirements.

The main objective of this work is to propose a new model that integrates Big Data into the decision-making process. Our model is a theoretical model that combines decision-modeling and Big Data analytics during the phases of the decision-making process. Big data analytics allows capturing, storing, and analyzing the huge amount of diverse data to extract knowledge that has the potential to improve the quality of decisions in organizations. The use of

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decision-modeling with Big Data support the communication and cooperation between the decision makers and Big Data analytical team during the phases of the model IDC to ensure a better understanding of the decision and the information required.

3. The presentation of DMP-BDE Model

DMP-BDE Model has been built depending on the results of some previous studies: B-DAD framework [7], DMN standard [23, 21, 22] and the integrated model discussed in [16]. DMP-BDE Model consists basically of four phases of the decision-making process proposed by Simon [20]: intelligence phase, design phase, choice phase and imple- mentation phase. Fig. 1. represents DMP-BDE model and TABLE 1 represents its phases, inputs, and outputs of each phase.

Fig. 1. DMP-BDE Model

Our model starts with the intelligence phase where the environment is scanned to identify and understand the decision to be made. In this phase, we define this decision and its requirements. These requirements include needed data, the knowledge to be extracted from this data that is capable of supporting this decision, and the outputs of other decisions necessary to make this decision. To identify all these requirements and understand them well, we propose to model our decision using DMN standard. This phase includes also collecting and preparing the needed data. In this context, we have divided this phase into two sub-phases: understanding & modeling decision and collecting & preparation data.

Fatma Chiheb et al. / Procedia Computer Science 151 (2019) 636–642 639 Author name / Procedia Computer Science 00 (2018) 000–000 3

• Simon later added a fourth phase, implementation phase where the chosen solution is put to work , and Mon- itoring can be considered as a fifth phase-a form of feedback [24].

4 Author name / Procedia Computer Science 00 (2018) 000–000

3. The presentation of DMP-BDE Model

Fig. 1. DMP-BDE Model

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Table 1. The phases of DMP-BDE Model, the input, and the output of each phase

Phases Sub-phases Inputs of phase Outputs of phases

Intelligence Understanding and Modeling Decision

Situation that call a decision (prob- lem statement)

Decision requirement diagram

Data Collection and Preparation

Decision requirement diagram Data prepared to be used in build model sub-phase

Design Build model Data prepared, Decision require- ment model

New information

Generate alterna- tives

New information, Decision require- ment model

Alternatives course of action

Choice Alternatives course of action, crite- ria of evaluation

Alternatives chosen

Implementation Alternatives chosen Result of decision

• Understanding and Modeling Decision: The decision that will be made is defined and understood, and then represented by the DRD model using the DMN standard. In this step, we propose to utilize DMN standard to model human decisions. Human decision-making could be broken down into a network of elements that defines the decisions and their requirements. The goal of this step is to use decision-modeling as a solution to represent decisions in a clear, simple and unambiguous way. In addition, the objective is to facilitate communication and collaboration between decision-makers and analytics team. Therefore, in this step, we can develop a DRD and describe it using a natural language rather than decision logic. In the case where the goal is to automate this decision, we use decision logic to describe DRD. The building of DRD is performed by applying iteratively four steps proposed by [22] identify decisions, describe decisions, specify decision requirements, and decompose & refine the model. • Data Collection and Preparation: this sub-phase is similar to identify big data, acquire/ store data, and

organize steps identified in the intelligence phase of B-DAD framework [7]. The data to be analyzed to derive the needed information are defined in the previous sub-phase as the input data in the DRD built. In this sub- phase, the different internal and external source that provides this data has to be identified. Then this data has to be acquired from these sources and stored. This data could come in different formats: image, text, video, audios, etc. Big data technologies provide many alternatives to support these steps such as Sqoop, Chukwa, Flume, Kafka to acquire the data from different source to be stored in storage and management tools that include HDFS and NoSQL databases such as: Hbase, Cassandra, CouchDB, Redis, and so on. After that, the data may be processed, queried, or visualized to gain a general understanding and description of the data using many tools such as: SparkSQL, Hive, and Pig for querying the data; SparkR for applying statistics; and Tableau for visualization so on.

The second phases of the decision-making process is the design phase where possible courses of action are invented, developed and analyzed to handle the situation that requires the decision. This phase could also be broken down into two sub-phases: Build models and Generate alternatives.

• Build model: where models for Big Data analytics are built to extract the knowledge required for this decision; this knowledge has already been identified and represented in decision requirements model. Building a model pass through two phases proposed by [5], namely: model planning and building model ( applying the model planned). In the model planning, a technique, or a short list of candidate techniques that will be used to build the model is identified and the data is explored to identify the variables suitable for this model. The techniques to be applied are chosen based on the objective of the model and the type of data available. Subsequently, data sets for testing, training, and production purposes are developed and the model is built and executed in building

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model. There are many analytical techniques could be applied to build analytical models in this step such as: data mining techniques (classification, clustering, regression, association rules), machine learning techniques, as well as, text analysis, social network analysis and sentiment analytics [7]. There are many big data tools could be used in this step such as: Mahoot, Spark MLlib, H2O, SAMOA, and so on. • Generate alternatives: in this sub-phase the decision makers rely on the new information, their knowledge

and their experiences to propose alternatives. Besides, the criteria which will be used to judge and evaluate each alternative are defined [16].

The following phase is the choice phase. After defining alternatives and criteria for evaluation, these alternatives are evaluated to know the impact that will have each one of them on business. To evaluate the alternatives, the reference [16] proposes the implementation of a DSS to predict the most adequate solutions among the alternatives proposed. In addition, the visualization, reporting, dashboards, what-if scenarios, simulation of solutions and others techniques can be used to evaluate these alternatives [7]. Based on the evaluation findings, decision makers choose an alternative or a set of alternatives that will solve the problem.

Thus, the final stage of the decision-making process is the implementation phase where the selected alternatives are actually implemented.

4. Discussion

This work provides a theoretical model that integrates Big Data analytics into decision-making process phases. The main contribution of this theoretical model is that it allows obtaining insights from Big Data during decision-making process phases to improve the quality of decisions in organizations.

The most important difference between our model that is known as DMP-BDE Model and previous work is that DMP-BDE Model utilizes decision-modeling using DMN standard with Big Data during decision-making process. A DRD is developed in the first phase. Then, it is used as a guideline to understanding how decisions are made and their requirements in the following phases. The use of decision-modeling using DMN in our model allows:

• Facilitating the communication and cooperation between the decision makers and Big Data analytical team during decision-making processs phases, therefore a better understanding to the business problem or opportunity by building a shared understanding of the decisions and their requirements. • Enable an efficient application of Big Data Analytics projects in the organisation: building the decision re-

quirements model by defining decision and identifying potential analytic knowledge that might be helpful in supporting this decision ensures that the right analytics required is identified before analytics are developed [23]. • Documenting decisions requirements thus, enabling organization reusing knowledge from project to project

[22].

The limitation of this work is that DMP-BDE Model stills a theoretical model that bases on a theoretical study without applying the proposed model to solve a real decision problem in a Big Data environment. However, this study highlights the importance of the integration of Big Data and decision-modeling within the decision-making process in the organizations to be applied and evaluated in our future works.

5. Conclusion

Big data is playing a central role in nearly every domain of our modern society. Nowadays, the analysis of Big Data is applied in a large number of fields and sectors such as healthcare, finance, business, education, marketing etc. The analysis of data created day-to-day provides new information. These latter has the potential to enhance the decision-making process, thus improve the quality of decisions and achieve a competitive advantage for organizations. The objective of our study is to help the decision makers taking advantage of Big Data to improve the quality of their decision. Consequently, we aim to enhance the decision-making process in the organization by proposing DMP- BDE Model that integrates Big Data analytics during the phases of the decision-making process. Relying on the

Fatma Chiheb et al. / Procedia Computer Science 151 (2019) 636–642 641 Author name / Procedia Computer Science 00 (2018) 000–000 5

Table 1. The phases of DMP-BDE Model, the input, and the output of each phase

Phases Sub-phases Inputs of phase Outputs of phases

Intelligence Understanding and Modeling Decision

Situation that call a decision (prob- lem statement)

Decision requirement diagram

Data Collection and Preparation

Decision requirement diagram Data prepared to be used in build model sub-phase

Design Build model Data prepared, Decision require- ment model

New information

Generate alterna- tives

New information, Decision require- ment model

Alternatives course of action

Choice Alternatives course of action, crite- ria of evaluation

Alternatives chosen

Implementation Alternatives chosen Result of decision

6 Author name / Procedia Computer Science 00 (2018) 000–000

and their experiences to propose alternatives. Besides, the criteria which will be used to judge and evaluate each alternative are defined [16].

Thus, the final stage of the decision-making process is the implementation phase where the selected alternatives are actually implemented.

4. Discussion

[22].

5. Conclusion

642 Fatma Chiheb et al. / Procedia Computer Science 151 (2019) 636–642 Author name / Procedia Computer Science 00 (2018) 000–000 7

results of the previous study, we build our model that combines Simon's decision-making process, Big Data analytics, and decision-modeling using DMN standard. The major contribution of our study is the modeling of decisions. Our proposed model starts with understanding and modeling the decision to be made. Then, this model serves as guidelines for decision makers and Big Data analytical team during the following phases. Therefore, the modeling of decision using DMN standard that presents the decision and their requirements in clear, simple, unambiguous way allows the communication and collaboration between decision-makers and data analytical team. Our model is still a theoretical model built on the results of previous studies. For our future work, we plan to evaluate and develop our model.

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