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ScienceDirect
<\/span>Available online at <\/span>www.sciencedirect.com
<\/span>Procedia Computer Science 141 (2018) 271\u2013277
<\/span>1877-0509 <\/span>\u00a9 <\/span>2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (<\/span>https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/<\/span>)
Selection and peer-review under responsibility of the scientific committee of EUSPN 2018.
10.1016\/j.procs.2018.10.181
<\/span>\u00a9 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (<\/span>https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/<\/span>)
Selection and peer-review under responsibility of the scientific committee of EUSPN 2018.
<\/span>Available online at <\/span>www.sciencedirect.com
<\/span>ScienceDirect
<\/span>Procedia Computer Science 00 (2018) 000\u2013000
www.elsevier.com\/locate\/procedia
1877-0509 \u00a9 2018 The Authors. Published by Elsevier Ltd.
<\/span>This is an open access article under the CC BY-NC-ND license (<\/span>http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/<\/span>).
Selection and peer-review under responsibility of the scientific committee of EUSPN 2018
<\/span>The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks
(EUSPN 2018)
<\/span>Exploring Big Data Governance Frameworks
<\/span>Ali Al-Badi<\/span>a <\/span>*, Ali Tarhini<\/span>a<\/span>, Asharul Islam Khan<\/span>a
<\/span>a <\/span>Department of Information Systems,, Sultan Qaboos University, Muscat, P.O. Box 20, Al-Khodh 123, Oman
<\/span>Abstract
<\/span>The recent explosion in ICT and digital data has led organizations, both private and public, to efficient decision-making.
Nowadays organizations can store huge amounts of data, which can be accessible at any time. Big Data governance refers to the
management of huge volumes of an organization\u2019s data, exploiting it in the organization\u2019s decision-making using different
analytical tools. Big Data emergence provides great convenience, but it also brings challenges. Nevertheless, for Big Data
governance, data has to be prepared in a timely manner, keeping in view the consistency and reliability of the data, and being
able to trust its source and the meaningfulness of the result. Hence, a framework for Big Data governance would have many
advantages. There are Big Data governance frameworks, which guide the management of Big Data. However, there are also
limitations associated with these frameworks. Therefore, this study aims to explore the existing Big Data governance frameworks
and their shortcomings, and propose a new framework. The proposed framework consists of eight components. As a framework
validation, the proposed framework has been compared with the ISO 8000 data governance framework.
\u00a9 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (<\/span>http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/<\/span>).
<\/span>Keywords: <\/span>Big Data; Big Data model; Big Data governance; Data management; Big Data governance framework; Big Data analytic;
<\/span>1. Introduction
<\/span>Nowadays, Organizations and social entities generate large amounts of structured and unstructured data referred
to as Big Data. Data growth is very fast and some may have increased over 100% such as in healthcare,
pharmaceuticals, energy, telecommunications, and transportation [1]. Big Data is a collection of large data sets that
contain massive and complex data. Big Data holds huge volumes of sets of data, measured in zettabytes and is
derived from a variety of sources [2]. Big Data refers to \u201cdata that exceeds the processing capacity of conventional
<\/span>* Corresponding author. Tel.: +968-99245674
<\/span>E-mail address: <\/span>[email\u00a0protected]<\/span>
<\/span>
272 <\/span>Ali Al-Badi et al. \/ Procedia Computer Science 141 (2018) 271\u2013277
Author name \/ Procedia Computer Science 00 (2018) 000\u2013000 <\/span>2
<\/span>database systems. The data is too big, moves too fast, or doesn\u2019t fit the structures of your database architectures\u201d
[3].The Big Data can be structured, un-structured or semi-structured. The examination and analysis of Big Data is
termed \u2018Big Data analytics\u2019 [4]. Big Data consists of phases: Big Data generation, Big Data acquisition, Big Data
storage, and Big Data analytics [5]. In the generation phase, several data sources generate huge volumes of data. The
Big Data acquisition includes data collection, data transmission, and data pre-processing. \u2018Big Data storage\u2019 refers
to the storage and management of large-scale datasets while achieving availability and reliability of data accessing.
Finally, Big Data analytics involves the analysis and forecasting of market trends, products, and services. Big Data
can be used in many applications in different fields such as healthcare, education, management, and logistics, etc.
The Big Data is used for crime prevention and counterterrorism in the United States, while in Korea it has been
implemented to support smart government operations and strategic planning [6]. Big Data systems enable the
dynamic analysis of data. Big Data has many issues and challenges due to the huge data sets. Companies that are
dealing with Big Data face challenges such as management, processing, and security because of using Big Data.
Moreover, there are issues of capturing, analysis, storage, searching, sharing, visualization, transferring and privacy
violations [4].
Data governance refers to the policies and procedures adopted in order to manage data in an organization [7]. A
\u2018data governance framework\u2019 is defined as \u201ca set of processes that ensures that important data assets are formally
managed throughout the enterprise\u201d [8]. Data governance provides the right sets of data to the right people whenever
the need arises, so that the right decisions can be made [9]. Data governance needs a framework for managing
information [10]. The data governance helps an organization with data standardization, efficient business policy
formulation, and defining the roles of stakeholders [6]. The Big Data managers face problems in cleaning,
governing, and managing the data [11]. The Big Data problem needs to be managed [12]. The enterprises
implementing Big Data need a new set of governance policies [13]. A strong Big Data governance program is
required, this being the foundation of information management [14]. The Big Data governance must account for
stewardship, information governance, data definition and usage standards, master data management, metadata
management, data lifecycle management, risk, and cost containment [15]. Big Data governance encompasses the
data governance program consisting of optimization policies, privacy, and monetization of the Big Data according to
the set objectives [14]. The data governance principles need to be defined [7], and these principles are essential in
data governance communication and authorization [16]. A Big Data governance framework is important in framing
policies, processes and standards for effectively managing and ensuring the availability, usability, integrity,
consistency, auditability and security of Big Data [17]. The governance of Big Data is crucial to the success of an
organization where huge amounts of data are processed. A Big Data governance framework is required to manage
the Big Data in an organization. There are Big Data governance frameworks which guide the management of Big
data. However, there are also limitations associated with these frameworks. Therefore, this study aims to identify the
existing Big Data governance frameworks and their shortcomings, and to propose a new framework.
The article is divided into five sections. Section 2 is about the research method. Section 3 corresponds to
motivation, existing frameworks and their shortcomings. Section 4 proposes the conceptual framework. The last
section concludes with the outcomes.
<\/span>2. Research method and protocol followed
<\/span>In this study, the research method used consists of four steps. The first step mainly involved a literature review
with the objective of generating ideas and identifying problems. In the second step, the articles were filtered, and
selected articles were analyzed thoroughly in terms of components and characteristics of the Big Data governance
frameworks. In this step, the initial search, by applying the simple and advanced query on the search terms \u201cBig
Data governance\u201d, \u201cBig Data governance framework\u201d, \u201cBig Data governance model\u201d in online databases (Google
scholar, Scopus, Science direct, Springer, and IEE explore), 200 articles published between 2008 and 2018 were
produced. The number was reduced to 110 after removing duplicates and non-English language articles. The articles
were further filtered based on relevant title and abstract. About 40 articles were then left for study. Finally, out of
the 40 articles, only 12 articles were actually talking about the Big Data governance frameworks. The articles were
filtered based on the set criterion of uniqueness, completeness, accuracy, time framed study, and accessibility<\/span>
Ali Al-Badi et al. \/ Procedia Computer Science 141 (2018) 271\u2013277 <\/span>273
<\/span>Author name \/ Procedia Computer Science 00 (2018) 000\u2013000 <\/span>3
<\/span>(several articles were inaccessible as full articles, such as [18]). The filtered articles were thoroughly analyzed and
interpreted.
The third step was the building of a new Big Data governance framework to overcome the
shortcomings\/limitations of the existing ones. The last step was a comparative analysis and framework validation
(identifying the relationship between the components of the proposed framework and the components of the
existing frameworks).
<\/span>3. Motivation and relevant studies on Big Data governance frameworks
<\/span>The concept of Big Data has been strongly leveraged and has become a driver for innovation [19]. Big Data
governance is a matter of critical importance for every organization that relies on data to drive business value.
Successful companies capitalize on their organizational data assets through effective understanding. Working with
Big Data raises new challenges and risks, such as ensuring secure access to data, and policies to govern the
uninterrupted upstream and downstream of data flow. The data governance is a part of data management, which
includes numerous other concepts and practices as well [20]. The data governance helps enterprises to improve and
maintain data quality and their use [21]. Data governance has rapidly gained in popularity [22; 23] and is now
considered to be an emerging area [24]. It is an emerging subject in the information systems (IS) field. In recent
years, the volume of data used within organizations has increased dramatically, playing a critical role in business
operations [25]. The structured data is easily accessed by tools like SQL, while for unstructured and semi structured
data, profiling tools are used [26; 22]. The existing data governance works on structured data while Big Data
governance incorporates both structured and unstructured data [6]. The huge volume of data forces the use of IT
methods in analysis and interpretation of Big Data [27]. The Big Data has introduced many challenges into
organizations [28]; for example, privacy and security in terms of personal information leakage and the monitoring of
customers\u2019 private lives [6].
According to Soares (2013b), using a Big Data governance forecasting and decision in the business is quickly
made irrespective of source, type, and speed of data or information received [29]. In particular, the data influences
both operational and strategic decisions. How to govern these data has become critical [22]. The data governance is
a serious concern for successful implementation and risk reduction, but most of the time it is ignored by
organizations [22; 30]. Not only the corporate data integrity and data quality is at risk but also IT professionals are
facing problems in the transition from existing data sets to Big Data because of this lack of a Big Data governance
framework [9]. The existing data governance faces challenges in the light of Big Data cases [31]. Too little attention
is paid to Big Data governance [32]. There is a need to establish Big Data governance frameworks in enterprises for
decision making [33]. Thus, a strong Big Data governance framework is crucial to the success of any Big Data
initiative and the management of that data. Unfortunately, most of the Big Data technologies do not offer data
governance functionality.
Each industry can be driven by the Big Data analytic such as marketing, customer service, information security,
or information technology. Big Data analytics support strategic and competitive decisions [34]. There are very few
studies on regulatory issues and the governance of Big data; most of the studies are on Big Data and analytics,
cloud, the Internet of things, mobility or social media, algorithms, and architecture [27]. The data governance
framework contains five inter decision domains such as data principles, data quality, metadata, data access,
and data life cycle [35]. In [22] the researcher has provided a framework for data governance based on five
inter domains. The search query produced many articles, and the studies relevant to Big Data governance
have been investigated thoroughly. There are 12 important articles on the Big Data governance framework as shown
in Table 1.
<\/span>Table1. Big Data governance frameworks
<\/span><\/p>\n\n\n\n\n\n\n
Frameworks <\/span><\/td>\nYear <\/span><\/td>\nComponents\/ characteristics<\/span><\/td>\n<\/tr>\n
BGF1-[36] <\/span><\/td>\n2018 <\/span><\/td>\nObjective, strategy (personal information protection strategy, data quality, and the data disclosure\/accountability
strategy), components (organization, standards and guidelines, policies and process), IT infrastructure (audit and
control, Big Data infrastructure).<\/span><\/td>\n<\/tr>\n
BGF2-[37] <\/span><\/td>\n2017 <\/span><\/td>\nData consumers, self-provisioning data portal, optimize and compute, data infrastructure or tired storage<\/span><\/td>\n<\/tr>\n
BGF3-[38] <\/span><\/td>\n2017 <\/span><\/td>\nData analytic, data querying, distributed data processing, distributed data storing, data acquisition.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

274 <\/span>Ali Al-Badi et al. \/ Procedia Computer Science 141 (2018) 271\u2013277
Author name \/ Procedia Computer Science 00 (2018) 000\u2013000 <\/span>4
<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
BGF4-[39] <\/span><\/td>\n2017 <\/span><\/td>\nGovernance objectives, the top-level design, governance objects, governance methods, the internal and external
environments and contributing factors.<\/span><\/td>\n<\/tr>\n
BGF5-[40] <\/span><\/td>\n2016 <\/span><\/td>\nQuality and consistency, policies and standards, security and privacy, compliance, retention and archiving.<\/span><\/td>\n<\/tr>\n
BGF6-[26] <\/span><\/td>\n2016 <\/span><\/td>\nOrganization, metadata, privacy, data quality, business process integration, master data integration, information
lifecycle management.<\/span><\/td>\n<\/tr>\n
BGF7-[18] <\/span><\/td>\n2015 <\/span><\/td>\nBig Data governance framework (content in accessible).<\/span><\/td>\n<\/tr>\n
BGF8-[31] <\/span><\/td>\n2015 <\/span><\/td>\nDiscover, define, apply, measure and monitor.<\/span><\/td>\n<\/tr>\n
BGF9-[29] <\/span><\/td>\n2013 <\/span><\/td>\n(a) a maturity assessment to determine readiness for data governance, (b) a business case to justify implementing
data governance, and (c) a roadmap to guide the data governance implementation.<\/span><\/td>\n<\/tr>\n
BGF10-[41] <\/span><\/td>\n2013 <\/span><\/td>\nEstablish difference between traditional data and Big Data governance, establish basic rules for where new data
governance can be applied, establish processes for graduating the products of data science to governance, and
establish a set of tools to make governing Big Data feasible.<\/span><\/td>\n<\/tr>\n
BGF11-[17] <\/span><\/td>\n2012 <\/span><\/td>\nStrategy, organizations, policies processes and standards, measurement and monitoring, technology,
communication.<\/span><\/td>\n<\/tr>\n
BGF12-[42] <\/span><\/td>\n2012 <\/span><\/td>\nBig Data types (i.e. web and social media, machine-to-machine (M2M), big transaction data, biometrics, and
human-generated), Information governance disciplines (i.e. organization, metadata, privacy, data quality, business
process integration, master data integration, and information lifecycle management), industries and functions (i.e.
marketing, customer service, information security, or information technology).<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Morabito in his study describes Big Data maturation models, organizational benefits and challenges without any
detail of a framework [18]. The Gartner promotes the creation of a Big Data governance framework consisting of
top management, IT tools and infrastructure, accounting to ensure controls, and business end users [9]. A Big Data
framework for handling urban governance issues and data analysis algorithms has been provided in [43]. In [14] the
researcher highlights the data governance frameworks for risk reduction (i.e. Big Data implementation), and
postulates a similar governance framework for Big Data [14]. Many researchers propagated the idea of using
traditional data governance attributes for Big Data (i.e. organization, metadata, business process integration, and
information lifecycle management).
<\/span>4. Conceptual framework and discussions
<\/span>It is important to have a Big Data governance framework. For smooth transitions to Big data, a data governance
framework can play a significant role and contribute to success [44]. The data generated are often in the range of
petabytes and many organizations have a lack of expertise to manage and operate them [45]. Big Data governance
solutions have an immediate impact across all enterprises and business operations. Big Data management without a
Big Data governance framework is difficult [17].
The proposed conceptual Big Data governance framework consists of eight components (Fig. 1). These
components are: identify organization structure, identify relevant stakeholders, identify the scope of Big data, set the
policies and standards, optimize and compute, measure and monitor quality, store the data, communicate and
manage the data. The information governance principles have been used in the proposed framework. The seven core
principles of information governance guidelines (i.e. organization, metadata, privacy, data quality, business process
integration, master data integration, and information lifecycle management) are also applicable to Big Data
governance [26].
<\/span>Fig 1. The proposed Big Data governance framework
<\/span>The organization and its structure influence the Big Data governance decisions. The organization\u2019s structure
requires enhanced study. Big Data governance should be aligned with the objectives and vision of the organization
[6]. Therefore, organization structure has been taken as a component. Another key element is the identification of
<\/span>Big Data governance
framework
Identify organizations
structure
Stakeholders selection
Big Data scope determination
Measure and monitor quality Optimize and compute Policies and standards setting
Data storage
Communictaion and data
management<\/span>
Ali Al-Badi et al. \/ Procedia Computer Science 141 (2018) 271\u2013277 <\/span>275
<\/span>Author name \/ Procedia Computer Science 00 (2018) 000\u2013000 <\/span>5
<\/span>relevant stakeholders in Big Data governance such as data scientists, data analysts, business steward leads, data
stewards, steering committee, etc. The next step is to understand the scope of Big Data under consideration, and
check to see if the scope for it that applies to the concerned organization has been defined. Many problems arise
with Big Data due to inadequate technologies to process it effectively [46]. The policies, rules, and standards
corresponding to data capture, management, consumption, privacy, security, risk, retention, regulatory compliance
and data classification requirements need to be framed. Big Data governance includes the policy of data
optimization, privacy protection and data realization to Big Data [26]. Whether or not the framed policies are
consistent with those for traditional systems needs to be checked. The optimization and computation consists of data
acquisition and data transformation. The organizations can benefit from analysing the data [47; 48]. The measuring
and monitoring of Big Data quality should be the top priority. The Big Data managers should check and remediate
issues of inconsistent or invalid data in the Big Data analytics pipeline. They should track every change from the
original conception to the final visualization. The Big Data governance produces quality output [18]. The data must
be cleaned before analysis in order to answer the questions [49]. Preparation and analysis is very important for
quality data [50]. The data is stored in a secured location, while at the same time being accessible when required.
Finally, the outputs are communicated and delivered to the clients. The selected studies (i.e. BGF1 to BGF12) on
Big Data frameworks have been compared with the ISO 8000 data governance framework [51] standard to identify
the gaps in the existing frameworks. Table 2 shows the comparison with ISO 8000 standard, where FR: full
representation, PR: partial representation. BGF7 framework is not accessible, so comparison is not possible.
<\/span>Table 2. Big Data governance frameworks comparison with ISO 8000 standard
<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ISO 8000 data
governance
framework<\/span><\/td>\n		Planning<\/span><\/td>\nProcess
identification<\/span><\/td>\n		Data
identification<\/span><\/td>\n		Stakeholders
identification<\/span><\/td>\n		Technology
identification<\/span><\/td>\n		Implementation
and evaluation<\/span><\/td>\n		Conformance
to the quality<\/span><\/td>\n		Summary
(FR %)<\/span><\/td>\n<\/tr>\n
BGF1 <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n71 %<\/span><\/td>\n<\/tr>\n
BGF2 <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\n43%<\/span><\/td>\n<\/tr>\n
BGF3 <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n14%<\/span><\/td>\n<\/tr>\n
BGF4 <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n14%<\/span><\/td>\n<\/tr>\n
BGF5 <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\n14%<\/span><\/td>\n<\/tr>\n
BGF6 <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\n57%<\/span><\/td>\n<\/tr>\n
BGF7 <\/span><\/td>\nNA <\/span><\/td>\nNA <\/span><\/td>\nNA <\/span><\/td>\nNA <\/span><\/td>\nNA <\/span><\/td>\nNA <\/span><\/td>\nNA <\/span><\/td>\nNA<\/span><\/td>\n<\/tr>\n
BGF8 <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\n57%<\/span><\/td>\n<\/tr>\n
BGF9 <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n29%<\/span><\/td>\n<\/tr>\n
BGF10 <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n43%<\/span><\/td>\n<\/tr>\n
BGF11 <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n57%<\/span><\/td>\n<\/tr>\n
BGF12 <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\nPR <\/span><\/td>\n43%<\/span><\/td>\n<\/tr>\n
Proposed framework <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nFR <\/span><\/td>\nPR <\/span><\/td>\nFR <\/span><\/td>\n87%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

5. Conclusion
<\/span>Big Data management has become a challenge in the IT and business fields due to a massive growth in the data,
which are often structured, un-structured, and semi structured in nature. The Big Data governance has recently
attained importance in organizations\u2019 decisions and policymaking. This article describes a comprehensive study on
the existing Big Data governance frameworks and their limitations. There are very few studies on Big Data
governance frameworks. In this study, a conceptual framework for Big Data governance has been proposed. The
framework has been derived through analysis of the existing governance problems in Big Data. There are eight
major components in the proposed frameworks. These components are the organizations\u2019 structure identification,
stakeholders\u2019 identification, identifying the scope of Big Data, setting the policies and standards, optimizing and
computing, measuring and monitoring quality, data storage, communication and data management. The proposed
framework and the existing frameworks have been compared with the ISO 8000 standard. The proposed framework
satisfies 87% of the ISO 8000 standard criteria. The framework is expected to be implemented in an organization for
validation in future.<\/span>
276 <\/span>Ali Al-Badi et al. \/ Procedia Computer Science 141 (2018) 271\u2013277
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ScienceDirect Available online at www.sciencedirect.com Procedia Computer Science 141 (2018) 271\u2013277 1877-0509 \u00a9 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/) Selection and peer-review under responsibility of the scientific committee of EUSPN 2018. 10.1016\/j.procs.2018.10.181 \u00a9 2018 The Authors. Published by Elsevier Ltd. This is … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-34","post","type-post","status-publish","format-standard","hentry","category-essaywr"],"_links":{"self":[{"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/posts\/34","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/comments?post=34"}],"version-history":[{"count":0,"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/posts\/34\/revisions"}],"wp:attachment":[{"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/media?parent=34"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/categories?post=34"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/academicwritersbay.com\/writings\/wp-json\/wp\/v2\/tags?post=34"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}