Towards Automated Management and Analysis of Heterogeneous Data

within Cannabinoids Domain

Kenji Koga

, Maria Spichkova

and Nitin Mantri

iSelect, Cheltenham, Australia

School of Science, RMIT University, Melbourne, Australia

Keywords:

Software Engineering, Data Integration, Health Systems, Biomedicine, Bioinformatics.

Abstract:

Cannabinoid research requires the cooperation of experts from various ﬁeld biochemistry and chemistry to

psychological and social sciences. The data that have to be managed and analysed are highly heterogeneous,

especially because they are provided by a very diverse range of sources. A number of approaches focused

on data collection and the corresponding analysis, restricting the scope to a sub-domain. Our goal is to

elaborate a solution that would allow for automated management and analysis of heterogeneous data within

the complete cannabinoids domain. The corresponding integration of diverse data sources would increase the

quality and preciseness of the analysis. In this paper, we introduce the core ideas of the proposed framework

as well as present the implemented prototype of a cannabinoids data platform.

1 INTRODUCTION

Since the beginning of the ﬁrst phytocannabinoids

characterisation in the 20

century, see Grotenher-

men (2004), and the ﬁrst studies using tetrahydro-

cannabinol and cannabidiol, we faced a boost in

research involving cannabinoids. With the use of

medicinal cannabis being legalised in a growing num-

ber of countries, several studies in different health ar-

eas have been conducted such as in inﬂammatory dis-

eases, see e.g., Hasenoehrl et al. (2017), neurologi-

cal disorders or related symptoms, see Solimini et al.

(2017); Pertwee (2012), cancer, see Pagano and Bor-

relli (2017); Naderi et al. (2018); Pastor et al. (2004);

Milano et al. (2017) and cardiovascular diseases, see

Mendizabal and Adler-Graschinsky (2007).

To conduct the cannabinoid research effectively

and efﬁciently, data from different sources have to

be considered. For example, a researcher interested

in ﬁnding the best treatment for a particular disease

has to analyse data on speciﬁc cannabinoid strains,

the treatment data and the corresponding effects that

patients or doctors have described. Since these data

are handled by different individuals and institutions,

which generally have their own data format, this task

requires the integration of multiple data sources that

are heterogeneous. Moreover, the diversity of the user

backgrounds requires the corresponding adjustments

of the system interface.

A number of approaches aimed to combine data

in areas such as pharmacology, see Gray et al. (2014);

Wishart et al. (2017); Samwald et al. (2011), and

health sciences, see Puppala et al. (2015); Reis et al.

(2018). Most of them applied some open standards

like OpenEHR

to present the collected data. This

solution is not applicable for the case of cannabinoids

research: we are dealing not with a single domain that

has already their data standards, but we have to collect

and integrate data from multiple heterogeneous sub-

domains. Thus, the challenges are not only in the in-

tegration of data collected from a single sub-domain,

e.g. health data records, but also in integration of mul-

tiple heterogeneous domains.

Contributions: In this work we propose a platform

for automated collection, management and analysis of

cannabinoids data. This platform will integrate data

from several cannabinoids data sub-domains in order

to provide means for higher quality research analy-

sis. We also present the implemented prototype of the

proposed platform.

Outline: The rest of this paper is organised as fol-

lows. Background and related work are discussed in

Section 2. Sections 3 and 4 introduce the core ideas

of the proposed cannabinoid data platform as well as

http://openehr.org/

Koga, K., Spichkova, M. and Mantri, N.

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain.

DOI: 10.5220/0007767405390546

In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), pages 539-546

ISBN: 978-989-758-375-9

539

its current implementation. Section 5 concludes the

paper and provides some future work directions.

2 BACKGROUND AND RELATED

WORK

In the cannabinoids domain, there are a number of

projects focusing on the acquisition of cannabinoid

data such as Strainprint

, SeedFinder

and Open

Cannabis Project

. The Strainprint project collects

personal data (user proﬁle), data on strains, inges-

tion methods and dosage. Its core objective is to keep

track of the effectiveness of treatments. The other two

projects focus on collecting and sharing information

about cannabis strains, without any objective to inte-

grate these data with any other type of data to identify

the most effective treatment using cannabinoids.

Sawler et al. (2015) analysed the strain data classi-

ﬁcation using DNA samples which showed that strain

names do not represent genetically unique variety.

Samples with identical strain names were more ge-

netically similar to samples with different names than

to identical ones. This demonstrates another issue

that has to be covered when developing a system for

management and analysis of cannabinoids data: re-

searchers cannot rely on strain names only, the genetic

similarity has to be taken into account.

In genomics, Pharmacogenomics Knowledge-

base, see Whirl-Carrillo et al. (2012), and Public

Health Genomics Knowledge Base, see Yu et al.

(2016), are open Web-based knowledge bases that

collect, curate and provide information about human

genetics and population health. They focus on pro-

viding high-quality information to support medicine-

implementation projects and population health, re-

spectively. To achieve this, they periodically extract

data from, e.g. scientiﬁc publications, using manual,

natural language processing and Machine Learning

techniques. They differ from our approach because

they are not fully automated as well as they are fo-

cused on a single domain.

In what follows we discuss the approaches that do

not focus on cannabinoids domain, but present some

computer science concepts related to our research –

big data analytics in healthcare, cloud-based solutions

for health information systems, etc. Luo et al. (2016)

conducted a literature review on big data applica-

tion in biomedical research and health care, focusing

on the big data application in four major biomedical

http://strainprint.ca

https://en.seedﬁnder.eu

http://opencannabisproject.org

sub-disciplines: bioinformatics, clinical informatics,

imaging informatics, and public health informatics.

The authors identiﬁed 68 relevant papers, and their

study demonstrated “While big data holds signiﬁcant

promise for improving health care, there are several

common challenges facing all the four ﬁelds in using

big data technology; the most signiﬁcant problem is

the integration of various databases.” To provide an

effective and efﬁcient solution for this problem is one

of the goals of the platform we propose.

Wang et al. (2015, 2018) presents a survey on 26

big data analytics cases in healthcare research ﬁeld

and derived some of the best practices. This anal-

ysis resulted in an architecture with 5 logical layers

including data collection, data aggregation, analytics,

information exploration and data governance. In the

Data collection layer, they have all data sources col-

lection such as structured, semi-structured and un-

structured data. Data aggregation layer deals with

data extraction and transformation. In the Analytics

layer, they process and analyze data using, for exam-

ple, MapReduce and data mining. MapReduce is a

programming paradigm and an associated implemen-

tation for processing and generating large datasets,

see Dean and Ghemawat (2008). MapReduce can

be also seen as the core of Apache Hadoop

, an

open source platform for the distributed processing

of structured, semi- and unstructured data. In Infor-

mation exploration layer, Wang et al. (2015, 2018)

proposed to generate reports, alerts and notiﬁcations

outputs derived from the Analytics layer. In Data gov-

ernance layer, they propose to deal with ethical, legal,

and regulatory challenges managing all the life-cycle

of data, security, privacy and policies. In our work,

we derived data heterogeneity architectural layers fol-

lowing these best practices adapted to the cannabinoid

data domain.

Yusuf et al. (2015) and Spichkova et al. (2015)

presented a model of a cloud-based platform and

its open-source implementation, which allows re-

searchers to conduct experiments requiring complex

computations over big data. This platform might be

integrated within Analytics and Visualisation Layer

of the architecture we propose, see Section 3.

Calabrese and Cannataro (2015) reviewed main

cloud-based healthcare and biomedicine applications,

especially focusing on healthcare, biomedicine and

bioinformatics solutions. The authors summarised

core issues and problems related to the use of such

platforms for the storage and analysis of patients data.

Bahga and Madisetti (2015) developed and ex-

tended Cloud Health Information Systems Technol-

ogy Architecture, which allows clinical data inte-

https://hadoop.apache.org

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

540

gration, access and analytics. It achieves integra-

tion through mapping source-speciﬁc format to a do-

main model. It supports formats, such as Health

Level-7 messages and Clinical Document Architec-

ture and raw ASCII. Once data schema matches the

domain model, the framework proceeds with a par-

allel MapReduce aggregation and transformation task

and write the result to HDFS storage. Data access and

analytics can be performed through Hadoop ecosys-

tem components such as Pig

or Hive

providing

seamless access to all the data inside the cloud us-

ing HCatalog from Hadoop. Since we will deal with

different kinds of domain standards, we will have to

research which are the main standards available in the

cannabinoid data research area and provide equiva-

lence of semantics. However, we will take advantage

of a similar cloud infrastructure approach in order to

get all the beneﬁts that it provides such as paralleliza-

tion of processing jobs, fault-tolerance and scalability.

eClims, see Savonnet et al. (2016), is another

example of an integration framework to deal with

data and schema variability in Biomedical Informa-

tion Systems (BIS). Since Biomedical research area

has to deal with the constant integration of increasing

number of databases and ontologies, they have cre-

ated eClims to facilitate the integration of new data

and extend data models at the same time assuring

quality using Databases and Semantic Web theory. In

this approach, the authors preferred a manual solution

for semantic analysis of collected data that were col-

lected from several providers, hence had a different

structure. The question of a full automation was still

open in that approach.

There are also several approaches to integrate het-

erogeneous pharmacology data such as the Life Sci-

ences Linked Open Data (LSLOD) cloud, but it stills

a difﬁcult task to acquire relevant results. It is nec-

essary to combine knowledge generated from drugs,

physiological functions in biological systems and un-

derlying biological interactions. This demands ef-

forts on integrating multiple heterogeneous sources,

perform manual entity reconciliation and disambigua-

tion, which are non-trivial and non-scalable tasks.

Kamdar and Musen (2017) developed a Platform for

Linked Graph Analytics in Pharmacology to per-

form integration of four different data sources from

LSLOD cloud, using a data model, to abstract all the

relevant mechanisms of drug relations, query feder-

ation, where SPARQL

queries are performed in all

sources to generate k-partite network and probabilis-

tic model to discover associations between, for exam-

https://pig.apache.org

https://hive.apache.org

https://www.w3.org/TR/rdf-sparql-query

ple, drugs and adverse drug reactions. In our work,

we will take advantage of their infrastructure on how

to deal with Semantic Web Technologies, i.e., their

data model and query federation in order to perform

data integration.

3 PROPOSED ARCHITECTURE

In this section, we discuss the core aspects of our

Cannabinoids Data Platform (CDP) for the manage-

ment and analysis of cannabinoids data. Figure 1

presents a layered architecture of the CDP.

In the Analytics and Visualization Layer, all

the concepts regarding user interaction are handled.

Users should be able to interact with the CDP through

different kinds of interfaces. We analyze and provide

corresponding functionalities required for each user

type. Interactive and integrated visualizations are pro-

vide in different options for visualizations in charts

and tables. To implement an efﬁcient search func-

tionality, data has to be prepared and indexed (in Data

Processing Layer). Like in the Data Capture Layer,

we have to consider here the speciﬁcs of Cannabi-

noids Domain to provide an easy-to-use interface that

allows to ﬁnd, order/rank, match and analyse the col-

lected data.

As the functionalities provided within this layer

are mostly focused on researchers, it makes sense to

apply the technologies that are already used in the cor-

responding research community. Thus, to support the

analysis and visualisation of the large amount of col-

lected data, we are going to apply a research-oriented

cloud computing platform Chiminey that provides

user-friendly interface for the computation/ analysis

set up, as well as visualisation of the calculation re-

sults as 2D or 3D graphs, see Yusuf et al. (2015) and

Spichkova et al. (2015). Monitoring and alerts has to

be provided to notify users about updates in the data

they are interested (through a previous subscription),

for example, new treatment data or changes in exper-

iments.

In the Data Processing Layer, data are prepared

and provided in the repository of processed data. This

repository contains a number of data sets, each of

them is specialised in providing data for a speciﬁc

usage. This is achieved through prior data cleaning

and correction, where useless data is removed, i.e.,

data that is not useful for a speciﬁc data set context,

and corrected if there is a possibility to do so. Other

important steps are data classiﬁcation, categorization

and indexing, as they provide a basis for better search

results as the data with similar features are kept to-

gether in an appropriate set and structured using Se-

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

541

Figure 1: Architecture of the CDP.

mantic Web technologies such as presented in Kam-

dar and Musen (2017). The processing infrastructure

used here is similar to the one proposed by Bahga

and Madisetti (2015). In the Data Aggregation Layer,

data of any different format in the cannabinoids do-

main are aggregated and stored. We are consider-

ing storage of some of the unstructured (Excel, Word,

PDF and images), semi-structured (JSON and XML)

and structured (MySQL and PostgreSQL) data since

we do not have full knowledge of all available data

types in the cannabinoid domain.

In the Data Capture Layer, data is extracted from

research data bases as well as publications or manu-

ally fed in by the users (growers, researchers, doctors/

treatment coordinators, and patients). The users can

provide their data in several ways, for example up-

loading ﬁles or typing information in a Web form (in

the case of researchers, doctors / treatment coordina-

tors, and growers) or using a mobile application (in

the case of patients). The user interface for patients

should be as straightforward and simple as possible,

as some of the patients might have not much experi-

ence in using mobile applications.

Since each user of the platform can provide their

data using different mechanisms and interfaces, some

of the data might be unstructured, depending on the

sub-domain:

• The data collected within the hospital and growers

sub-domains is always structured, as all the users

within these sub-domains can provide the data

only using the corresponding forms / interfaces.

Thus, if we would limit the platform to these sub-

domains, a data warehouse solution woulds be

sufﬁcient, see George et al. (2015).

• The vast part of the data collected within the re-

search sub-domain is unstructured and data ex-

traction becomes a challenge within this sub-

domain. This means that only the data lake so-

lution is applicable, see e.g., Soini et al. (2017);

Miloslavskaya and Tolstoy (2016); Fang (2015).

Also, the corresponding algorithms have to be devel-

oped to overcome this heterogeneity in data acquisi-

tion. A meta-format of the data should be applied,

so that all collected data can be represented within

this format providing a common basis for the further

analysis of data.

The Data Quality Layer is orthogonal to all the

other Layers and deals with the quality of data that

ﬂows through these layers. Thus, it is dedicated to

syntactical and semantic data analysis and veriﬁca-

tion, as well as quality assurance and usability as-

pects. One of important aspects is here also the track-

ing of data ﬂows through all layers to provide means

to reproduce data processing conducted in our archi-

tecture.

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

542

Figure 2: CDP: Architecture of the hospital sub-domain.

Figure 3: CDP: Flow Diagram for the hospital sub-domain (patient interface).

4 CANNABINOIDS DATA

PLATFORM

In this section, we introduce the current implemen-

tation of CDP. Currently, the prototype focuses on

the infrastructure required to collect data from the

users within the hospital and research sub-domains:

patients, doctors/ treatment coordinators and re-

searchers. Figure 2 presents the architecture of the

hospital sub-domain.

The prototype has two core interface components:

• mobile applications (both iOS and Android) de-

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

543

Figure 4: CDP: User interface for the hospital sub-domain (“Manage Forms” page, part of the functionality provided to doctor

/ treatment coordinator users).

Figure 5: CDP: User interface for the hospital sub-domain (“Manage Users” page, part of the functionality provided to doctor/

treatment coordinator users).

veloped for patients, see Figure 3; the applications

were built using Xamarin

, which provides cross-

platform compatibility between Android and iOS

platforms; Having a C#-shared codebase, devel-

opers can use Xamarin tools to write native An-

droid, iOS, and Windows apps with native user in-

terfaces and share code across multiple platforms.

• Web applications providing interfaces for doctors/

coordinators and researchers was developed us-

ing Angular 6, which is a TypeScript-based open-

source front-end web application platform

(see

Figure 4 for an example of the implemented Web

interfaces).

https://visualstudio.microsoft.com/xamarin

https://angular.io

Between the Front-end and Back-end, we imple-

mented an authentication system using OAuth2 with

refresh token grant type. In addition, the system is

using Transport Layer Security (TLS) cryptographic

protocols to provide communications security over a

computer network. In Back-end, MySQL Database is

used to store all resource data, and we implemented

an ASP.NET Web API project to communicate with

DB. The whole back-end including API (Application

Programming Interface) and data base are hosted in

Nectar Cloud

that provides free cloud services for

Australian Researchers.

Users have to register and log in before they are

provided a suitable interface for them. Patients can

https://nectar.org.au/research-cloud

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

544

add their treatment history including severity of con-

dition and effectiveness of particular formulations.

They can keep track of their treatment history to un-

derstand what works for their condition. Their as-

signed doctors have access to their treatment data to

customize the treatment.

Doctors can manage all their patient’s cases, allo-

cate to them corresponding questionnaires / forms to

ﬁll out on regular basis to collect data on the progress

of the treatment and its effectiveness. Doctors can add

comments and annotations for an individual case, as

well as add/ remove treatments for the patients.

Researchers, users that have to request higher

privileges in the system because they can browse any

patient case to help their research. They can also

search for a comprehensive investigation of strain

data, which also includes added advanced search

functionalities on the system.

The standard functionality to deal with the user

proﬁles is also covered within the current version of

the prototype: all users can update their proﬁle; pa-

tients and doctors can submit a request to become a

researcher with the CDP, where the researcher role

provides an access to anonymised data on treatments

and experiments being conducted.

5 CONCLUSIONS

In this paper, we presented the core ideas of the

Cannabinoids Data Platform, which goal is to in-

tegrate data from the complete cannabinoids do-

main, including research, hospital and growers sub-

domains. Dealing with multiple heterogeneous

sub-domains means additional challenges in col-

lecting and integrating heterogeneous and unstruc-

tured or semi-structured data from several sources,

as we cannot rely on a single data structure/ for-

mat or predeﬁned data standards. However, a meta-

structure/format can be introduces to integrate the

data. We implemented a prototype of the platform,

which currently has two types of interfaces: iOS and

Android mobile applications developed for patients,

and Web applications developed for doctors/ treat-

ment coordinators and researchers. The user inter-

faces in different sub-domains also differ, as we have

to take int account not only the type of data we col-

lect from each sub-domain but also the preferences

and skills of the users within the sub-domain.

Future work will be focused on (1) extending the

prototype to cover the growers sub-domain, (2) im-

plementing the data extraction algorithm from the

data lake within the research sub-domain, and (3)

analysing the usability and efﬁciency aspects for man-

agement and analysis of collected cannabinoids data.

ACKNOWLEDGEMENTS

We would like to thank Nidhi Chawla, Rachita

Chugh, Rochelle Maria Gracias, Jitender Singh

Padda, Songyan Li, Minh Tuan Nguyen, for their con-

tributions to the implementation of the prototype plat-

form.

REFERENCES

Bahga, A. and Madisetti, V. K. (2015). Healthcare data

integration and informatics in the cloud. Computer,

48(2):50–57.

Calabrese, B. and Cannataro, M. (2015). Cloud computing

in healthcare and biomedicine. Scalable Computing:

Practice and Experience, 16(1):1–18.

Dean, J. and Ghemawat, S. (2008). Mapreduce: simpliﬁed

data processing on large clusters. Communications of

the ACM, 51(1):107–113.

Fang, H. (2015). Managing data lakes in big data era:

What’s a data lake and why has it became popular in

data management ecosystem. In Int. Conference on

Cyber Technology in Automation, Control, and Intel-

ligent Systems, pages 820–824. IEEE.

George, J., Kumar, V., and Kumar, S. (2015). Data ware-

house design considerations for a healthcare business

intelligence system. In World congress on engineer-

ing.

Gray, A. J., Groth, P., Loizou, A., Askjaer, S., Brenninkmei-

jer, C., Burger, K., Chichester, C., Evelo, C. T., Goble,

C., Harland, L., et al. (2014). Applying linked data

approaches to pharmacology: Architectural decisions

and implementation. Semantic Web, 5(2):101–113.

Grotenhermen, F. (2004). Clinical pharmacodynamics of

cannabinoids. Journal of Cannabis Therapeutics,

4(1).

Hasenoehrl, C., Storr, M., and Schicho, R. (2017). Cannabi-

noids for treating inﬂammatory bowel diseases: where

are we and where do we go? Expert Review of Gas-

troenterology & Hepatology, 11(4):329–337.

Kamdar, M. R. and Musen, M. A. (2017). Phlegra: Graph

analytics in pharmacology over the web of life sci-

ences linked open data. In Int. Conference on World

Wide Web, pages 321–329.

Luo, J., Wu, M., Gopukumar, D., and Zhao, Y. (2016).

Big data application in biomedical research and health

care: a literature review. Biomedical informatics in-

sights, 8:BII–S31559.

Mendizabal, V. and Adler-Graschinsky, E. (2007). Cannabi-

noids as therapeutic agents in cardiovascular disease:

a tale of passions and illusions. British journal of

pharmacology, 151(4):427–440.

Milano, W., Padricelli, U., and Capasso, A. (2017). Re-

cent advances in research and therapeutic application

of cannabinoids in cancer disease.

Towards Automated Management and Analysis of Heterogeneous Data within Cannabinoids Domain

545

Miloslavskaya, N. and Tolstoy, A. (2016). Big data, fast

data and data lake concepts. Procedia Computer Sci-

ence, 88:300–305.

Naderi, J., Dana, N., Javanmard, S. H., Amooheidari, A.,

Yahay, M., and Vaseghi, G. (2018). Effects of stan-

dardized cannabis sativa extract and ionizing radiation

in melanoma cells in vitro.

Pagano, E. and Borrelli, F. (2017). Targeting cannabi-

noid receptors in gastrointestinal cancers for thera-

peutic uses: current status and future perspectives.

Expert Review of Gastroenterology & Hepatology,

11(10):871–873.

Pastor, M. G., Garcia, C. S., and Roperh, I. G. (2004). Ther-

apy with cannabinoid compounds for the treatment of

brain tumors. US Patent App. 10/647,739.

Pertwee, R. G. (2012). Targeting the endocannabinoid sys-

tem with cannabinoid receptor agonists: pharmaco-

logical strategies and therapeutic possibilities. Philo-

sophical Transactions of the Royal Society of London

B: Biological Sciences, 367(1607):3353–3363.

Puppala, M., He, T., Chen, S., Ogunti, R., Yu, X., Li, F.,

Jackson, R., and Wong, S. T. C. (2015). Meteor:

An enterprise health informatics environment to sup-

port evidence-based medicine. IEEE Transactions on

Biomedical Engineering, 62(12):2776–2786.

Reis, L. F., Ferreira, D. G., Maranhao, P. A., Cruz-Correia,

R., and Vieira-Marques, P. (2018). Integration through

mapping — an openehr based approach for research

oriented integration of health information systems.

In Conf. on Information Systems and Technologies,

pages 1–5.

Samwald, M., Jentzsch, A., Bouton, C., Kallesøe, C. S.,

Willighagen, E., Hajagos, J., Marshall, M. S.,

Prud’hommeaux, E., Hassanzadeh, O., Pichler, E.,

et al. (2011). Linked open drug data for pharmaceuti-

cal research and development. Journal of cheminfor-

matics, 3(1):19.

Savonnet, M., Leclercq, ., and Naubourg, P. (2016). eclims:

An extensible and dynamic integration framework

for biomedical information systems. IEEE Journal

of Biomedical and Health Informatics, 20(6):1640–

1649.

Sawler, J., Stout, J. M., Gardner, K. M., Hudson, D., Vid-

mar, J., Butler, L., Page, J. E., and Myles, S. (2015).

The genetic structure of marijuana and hemp. PLOS

ONE, 10(8):1–9.

Soini, E., Hallinen, T., Kekoni, A., Kotimaa, J., Nyk

anen,

M., Tirkkonen, J., and Tervahauta, M. (2017). Efﬁ-

cient secondary use of representative social and health

care data in ﬁnland: Isaacus data lake, analytics and

knowledge management pre-production project. Value

in Health, 20(9).

Solimini, R., Rotolo, M. C., Pichini, S., and Paciﬁci, R.

(2017). Neurological disorders in medical use of

cannabis: an update. CNS & Neurological Disorders-

Drug Targets (Formerly Current Drug Targets-CNS &

Neurological Disorders), 16(5):527–533.

Spichkova, M., Thomas, I. E., Schmidt, H. W., Yusuf, I. I.,

Drumm, D. W., Androulakis, S., Opletal, G., and

Russo, S. P. (2015). Scalable and fault-tolerant cloud

computations: Modelling and implementation. In Int.

Conference on Parallel and Distributed Systems (IC-

PADS), pages 396–404. IEEE.

Wang, Y., Kung, L., and Byrd, T. A. (2018). Big data an-

alytics: Understanding its capabilities and potential

beneﬁts for healthcare organizations. Technological

Forecasting and Social Change, 126:3 – 13.

Wang, Y., Kung, L., Ting, C., and Byrd, T. A. (2015). Be-

yond a technical perspective: Understanding big data

capabilities in health care. In 2015 48th Hawaii Inter-

national Conference on System Sciences, pages 3044–

3053.

Whirl-Carrillo, M., McDonagh, E. M., Hebert, J., Gong, L.,

Sangkuhl, K., Thorn, C., Altman, R. B., and Klein,

T. E. (2012). Pharmacogenomics knowledge for per-

sonalized medicine. Clinical Pharmacology & Ther-

apeutics, 92(4):414–417.

Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu,

A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Say-

eeda, Z., et al. (2017). Drugbank 5.0: a major update

to the drugbank database for 2018. Nucleic acids re-

search, 46(D1):D1074–D1082.

Yu, W., Gwinn, M., Dotson, W. D., Green, R. F., Clyne,

M., Wulf, A., Bowen, S., Kolor, K., and Khoury, M. J.

(2016). A knowledge base for tracking the impact of

genomics on population health. Genetics in Medicine,

18(12).

Yusuf, I. I., Thomas, I. E., Spichkova, M., Androulakis, S.,

Meyer, G. R., Drumm, D. W., Opletal, G., Russo, S. P.,

Buckle, A. M., and Schmidt, H. (2015). Chiminey:

Reliable computing and data management platform in

the cloud. In 37th International Conference on Soft-

ware Engineering (ICSE).

ENASE 2019 - 14th International Conference on Evaluation of Novel Approaches to Software Engineering

546