JSM Computer Science and Engineering

Functional Representations of Scientific Workflows

Editorial | Open Access | Volume 1 | Issue 1

  • 1. Department of Computer Science, Texas Tech University, USA
+ Show More - Show Less
Corresponding Authors
Noe Lopez-Benitez, Department of Computer Science, Texas Tech University, Lubbock, Texas, 79409-3104, USA

Lopez-Benitez N (2014) Functional Representations of Scientific Workflows. Comput Sci Eng 1(1): 1001.


Workflows describe not only a collection of component functions, but also their dependencies, which predefine a constrained order of execution. Scientific workflows are used to describe not only computational and service requirements but also the location of such services or computational units. Instruments in scientific laboratories, robots in remote inaccessible areas, a satellite unit in outer space, a set of databases, storage units as well as computational units, all provide services that must be orchestrated to satisfy an overall scientific objective. In this paper functional representations of workflows are discussed as a convenient alternative abstractions that can provide the basis for dynamic management of service requests; furthermore they can be regarded as a paradigm to organize and easily develop entire applications via the use of functional languages to explore not only fine-grained parallelisms, but also functional dynamic parallelisms suitable for grid and cloud execution environments.

Functional representations are proposed in the context of functional languages such as Haskell [1], Parallel Haskell [2], SequenceL [3], and others. Functional languages provide users the ability to specify and/or generate possible parallel operations for fine-grained computational platforms. Haskell has been extended to Cloud Haskell to provide message-passing support [4]. Translation of functional code may lead to finegrain parallelism; sequenceL, for example, generates n-tuples of independent computations that can be mapped into multiple independent threads of execution; consequently, fine-grain parallelisms can be mapped into high-level languages such as MCUDA suitable for multi-core execution platforms.

Background Work

Abstract-to-concrete workflow is a transformation that prevails in a cloud environment [5], where an orchestration process completes the mapping into a concrete web-based executable workflow. In [6] a series of workflow transformations referred to as ‘sequence’, ‘and split’ and ‘and join’ patterns lead to single node reductions. These transformations provide the basis for the reduction schemes discussed in this paper as applied to functional representations. The grouping of tasks reported in GridSolve [7] is intended to minimize transfer delays by either having a multiple-resource site execute all tasks or supporting the transfer of data between different parallel tasks executing in different service sites. Furthermore, fundamental work has been reported on workflow optimization for grid environments, on scheduling parallel clusters through Condor in [8], on schedulebased workflow balancing [9], on performance and overhead of high-performance applications [10], on task clustering for balanced workflows in [11] task clustering is of interest because the functional representation models described in this paper are based on partitioning an entire workflow into sub-workflows, similar to the heuristics reported in [12] and complemented with the integration of resource provisioning as reported in [13].

Functional Representation of Workflows

A workflow, used as a typical directed acyclic graph (DAG) is described by a set of vertices representing individual tasks and a set of edges representing data dependencies. It is possible to regard each task in a workflow as a functional unit depending on the execution of its predecessors. For example, if a task A is followed by independent tasks B and C in the workflow, then a notation [B,C]A will model not only such dependencies but also indicates that tasks B and C can be executed in parallel. Using this representational approach, a functional description of a workflow W can be described as:

W=\left [ T_{F1}\left [ .. \right ],T_{F2\left [ ... \right ],...,}T_{Fn}\left [ ... \right ] \right ]                         (1)

Where T_{Fi}\left [ ... \right ]represents a collection of nodes describing a path of dependent functions. If we let TFi represent a terminal node (function) in the workflow, then each TFi […] in equation (1) can be described as:

T_{Fi}\left [ T_{X}\left [ T_{Y}\left [ ... \right ]... \right ] \right ],i=1,...,n

Where Tx and Ty are nodes in one of the execution paths leading to terminal node TFi. Thus, each node in the workflow is expressed as a function of all previous nodes in its execution path. The collection of functions forms a queue of dependent functions that must be executed in sequence. Each queue structure includes an initial task i.e., with no incoming arcs, identified as a root task. A set of root tasks corresponds to independent tasks that can be dynamically scheduled for a parallel execution [14]. Consider for example the workflow shown in [Figure. 1a]. A functional representation can be derived from the workflow shown in Fig. 1b, where each node is expressed as a function of all previous computing nodes in its path. To maintain the order of execution each terminal node tails each queue derived from [Figure.1b] which, following a functional representation, can be expressed as follows:

W = [[F[D[B[A]]], G[D[B[A]],E[B[A]], C[A]] ]                   (2)

Removing the square brackets from equation (2) then a set of queues given by all paths in the workflow are shown:


The right most function in each queue corresponds to a “root” function and the tail function corresponds to a terminal node in the workflow. These structures can be easily obtained applying well-known depth-first search algorithms.

Systematic Partitioning of Workflows

Extracting sets of root functions leads to possible partitions of the original workflow. Consider for example the following partitions from the set of queues generated for the workflow in (Figure 1a)

A workflow and its functional representation derivation.

Figure 1: A workflow and its functional representation derivation.

1. { FDB, GDB, GEB, GC\} [A]

2. { FD, GD, GE \} [B,C][A]

3. [F, G][D, E][B, C][A]

Partition 1 is formed by extracting root A to the right. The set of roots {B, C} are shown as heads of the remaining set of queues and are extracted to the right as two parallel functions as shown in partition 2. Likewise the sequence shown in partition 3 shows sets of functions that are unique, i.e., no function is contained in any other set. Identifying partitions of a workflow composed of sequential and/or parallel patterns in a workflow may lead to a reduced representation without altering the functionality of the original workflow.

The notation used this far describes parallel functions separated by commas; otherwise, a sequential execution is indicated. Using square brackets enforces serialization. As illustrated, to preserve the dependencies explicit in each queue, root functions are always extracted to the right. Also, if different roots are extracted from different queues they are indicated as a parallel structure.

The following rules are intended to formalize the manipulation of workflows and obtain alternate representations whenever possible. The general description of sequential and parallel patterns assumes that xi , i = 1, …, n, identifies the ith node in a pattern with n number of nodes (functions).

Extraction Rules

Extracting common functions leads to the generation of parallel structures amenable to the reduction rules discussed this far.

1. Right Extraction Rule: This rule extracts a common function or a common composition to the right. Extracting a common function y results in a parallel composition in which y becomes a root node that precedes the parallel execution of all nodes x_{i}:

\left \{ X_{n}y,...,X_{2}Y,X_{1}Y \right \}=\left \{ \left [ X_{n},...,X_{2}X_{1} \right ]Y \right \}

Extracting an entire composition to the right leads to a sequence of two parallel compositions.

2. Left Extraction Rule: Extracting a node y to the left results in a parallel composition in which y is a terminal node following the parallel execution of all xi nodes:

\left \{Y X_{n},...,YX_{2},YX_{1} \right \}=\left \{Y \left [ X_{n},...,X_{2}X_{1} \right ]\right \}

Extracting an entire composition to the left also leads to a sequence of two parallel compositions.

Sequential Composition Rules

This composition describes a functional description of a workflow in which all nodes x1 to xn must be executed in sequence X_{n}\left [ X_{n-1}...X_{2}X_{1} \right ]; in this pattern xn is the terminal function and it is dependent on the sequential execution of all the functions in the set \left [ X_{n-1}...X_{2}X_{1} \right ]. The following rules apply to sequential embedded patterns:

3. Sequential Reduction Rule 1: A sequential composition of functions in the set \left [ X_{n}...X_{2}X_{1} \right ] can be embedded in a functional representation as follows:

X_{n}\left [ X_{n-1}...X_{2}X_{1} \right ],X_{n}\left [ Y \right ]

where Y\notin \left \{ X_{n}...X_{2}X_{1} \right \}. Then a reduction functionally equivalent is of the form:

X_{n}\left [ X_{n-1}...X_{2}X_{1},Y \right ]

4. Sequential Reduction Rule 2: This rule applies if the sequential composition of the set {xn ,…, x2 , x1 } is embedded in a functional representation of the form:

X_{n}\left [ X_{n-1}...X_{2}X_{1} \right ],Y\left [ X_{n-1}...X_{2}X_{1} \right ]

which can be reduced to the functionally equivalent form:

\left [ X_{n},Y \right ]\left [ X_{n-1}...X_{2}X_{1}\right ]

Parallel Composition Rules

A parallel-to-series pattern transformation to transform and reduce embedded parallel patterns. Using a parallel functional expression can be transformed into a series expression indicated as follows:

\left [ X_{n},...,X_{2},X_{1} \right ]= X_{n}...X_{2}X_{1}

A parallel pattern can appear in two forms. A join or a fork structure. A join structure is identified in the following form:

Y\left [ X_{n},...,X_{2},X_{1} \right ]

In this composition y is a terminal node that depends on the parallel execution of all functions in the set \left \{ X_{n},...,X_{2},X_{1} \right \}. Extracting to the left a common function results in a common n-degree node y which defines an embedded join structure.

The following pattern identifies a fork composition:

\left [ X_{n},...,X_{2},X_{1} \right ]Y

This composition shows that the set of parallel functions \left \{ X_{n},...,X_{2},X_{1} \right \} can be executed but only after node y (which is not in the parallel set) executes successfully.

5. Parallel Fork Composition Rule: A functional representation given as follows:

\left [ X_{n},...,X_{2},X_{1} \right ]X_{i},X_{k}Y

contains and embedded fork composition X'_{i}\left [ X_{n},...,X_{2},X_{1} \right ].if Y\notin \left \{ X_{n}...X_{2}X_{1} \right \}, and Y\notin \left \{ X_{n}...X_{2}X_{1} \right \}then the following reduced sequential compositions are functionally equivalent:

\left [ X_{n},...,X_{2},X_{1} \right ]\left [ X_{i},Y \right ]=\left [ X_{n},...,X_{2},X_{1} \right ]X_{i},Y

6. Parallel Join Composition Rule: If a functional description of a workflow is given as follows:

x_{i}\left [ x_{n},...,x_{2},x_{1} \right ],\left [ yx_{k} \right ]

Then for any pairY\notin \left \{ X_{n}...X_{2}X_{1} \right \} and X_{k}\in \left \{ X_{n},...,X_{2}X_{1} \right \}, the following sequential compositions are functionally equivalent:

\left [ y,x_{i} \right ]\left [ x_{n},...,x_{2},x_{1} \right ]\left [ x_{n},...,x_{2},x_{1} \right ]

To illustrate the application of these rules consider the workflow shown in (Figure 2a). The set of paths for this workflow are given as follows:


Extracting to the right the initial two roots results in the following partition:

{ FC, FD, FD, HD, HD, HE } [A, B]

Extracting each time the root functions from the remaining queues, the following sequence is reached:

[F. H] [C,D,E][A,B]

This partition set corresponds to three parallel compositions shown in (Figure 2a). Alternatively, the first two parallel compositions can be merged into a single partition as shown (Figure 2b). Using square brackets to separate different partitions then:

[F. H] [C,D,E][A,B] = [F. H] [ [C,D,E][A,B] ]

Applying the reduction rules described previously, alternate partitions can be derived as follows:

{ FC, FD, FD, HD, HD, HE } = {FC, FD, H[ D, E]}

= { F[C,D], H[ D, E] }

= { FCD, H[D,E] }

= { FCH [D,E] }

The overall expressions are shown in (Figure 2c) and [Figure 2d] which correspond to the following functionally equivalent representations:

FCH [D,E] [A,B] = FCH [ [D,E] [A,B] ]

Illustration of workflow partition rules. a) A workflow with a sequence  of three parallel compositions, b) with a sequence of two parallel compositions,  c) with a sequence of three compositions, two are parallel compositions, the  third one is a series composition, and d) with a sequence of two compositions  with reduced data transfers.

Figure 2: Illustration of workflow partition rules. a) A workflow with a sequence of three parallel compositions, b) with a sequence of two parallel compositions, c) with a sequence of three compositions, two are parallel compositions, the third one is a series composition, and d) with a sequence of two compositions with reduced data transfers.


This paper illustrates the feasibility of generating alternate representations of workflows. Partitions are derived based on a functional representation of the original workflow to which a set of reduction rules are systematically applied. Each partition corresponds to a sub-workflow represented by a composition in the functional representation. Each composition therefore can be orchestrated for submission and execution in a grid or a cloud environment. Resource provisioning can be functionally integrated for each composition. As each sub-workflow demands fewer resources for a shorter amount of time, a particular set of submissions can be optimized to require less data transfers, or to seek a balanced computational and data exchange requirements. The workflow partitioning heuristics reported in [12] and the integration of resource provisioning reported in [13] provide a context in which the partitioning rules discussed in this paper can be useful. In addition, a functional representation addresses coarse levels of granularity present in small (desktop) applications written using a functional language or any modern language with functional expressiveness.


1. Hudak P,  Hughes J, Jones S P,  Wadler P.  A History of Haskell: Being Lazy with Class, The Third ACM SIGPLAN History of Programming Languages Conference (HOPL-III). 2007.
2. Marlow S, Parallel. Concurrent Programming in Haskell. version 1.2, Mircrosoft Research Ltd., Cambridge, U.K.. 2012.
3. Cooke D E, Rushton N J, Nemanich B, Watson R G,  Andersen P. Normalize, Transpose, and Distribute: An Automatic Approach for Handling Nonscalars. ACM Transactions on Programming Language Systems. 2008.
4. Epstein J, Black A P, Peyton-Jones S. Towards Haskell in the Cloud}, ACM Haskell'11. 2011.
5. Juve G, Deelman E, Cafaro M, Alisio G.  Scientific Workflows in the Cloud, in Grids, Clouds and Virtualization, Compute Communications and Networks. Springer-Verlag. 2011; 71-91.
6. Jaeger, M.C. Rojec-Goldmann, G. Muhl, G. QoS Aggregation in Web Service Compositions, e-Technology, e-Commerce and e-Service. 2005; 181:185.
7. Li Yinan, YarKhan Asim, Dongarra Jack, Seymour Keith, and Hurault Aurèlie, Enabling workflows in GridSolve: request sequencing and service trading, the Journal of Supercomputing, 2013; 3:1133-1152.
8. Singh G, Kesselman C, Deelman E, Optimizing Grid-Based Workflow Execution, Journal of Grid Computing 2006; 3:201-219.
9. Rajakumar S, Arunachalam V P, Selladurai V. Workflow Balancing Strategies in Parallel Machine Scheduling, International Journal for Advanced Manufacturing Technology. 2004; 366-374.
10. Mehrotra P, Djomehri J, Heistand S, Hood R, Jin H, Lazanoff A. Performance Evaluation of Amazon Elastic Compute Cloud for NASA High-performance Computing Applications, Concurrency and Computation Practice and Experience. 2013.
11. Chen W, Ferreira da Silva R, Deelman E, Sakellariou R. Balanced Task Clustering in Scientific Workflows, 9th International Conference on eScience, Beijin. China. 2013.
12. Chen W, Delman E. Partitioning and Scheduling Workflows across Multiple Sites with Storage Constraints, Workshop on Scheduling for Parallel Computing, 9th Intl. Conf. on Parallel Processing and Applied Mathematics. 2011.
13. Chen W, Delman E, Integration of Workflow Partitioning and Resource Provisioning, 2012 12th IEEE/ACM International Symposium on Cluster. Cloud and Grid Computing. 764-768.
14. Lopez-Benitez N, Andersen P, Dynamic Structures for the Management of Complex Applications in Grid Environments, Proceedings of the 2009 International Conference on Grid Computing and Applications. 2009; 80-85.

Lopez-Benitez N (2014) Functional Representations of Scientific Workflows. Comput Sci Eng 1(1): 1001.

Received : 16 Dec 2013
Accepted : 17 Dec 2013
Published : 18 Dec 2013
Annals of Otolaryngology and Rhinology
ISSN : 2379-948X
Launched : 2014
JSM Schizophrenia
Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
Journal of Substance Abuse and Alcoholism
ISSN : 2373-9363
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Bioinformatics, Genomics and Proteomics
ISSN : 2576-1102
Launched : 2014
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
Journal of Sleep Medicine and Disorders
ISSN : 2379-0822
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
Journal of Cardiology and Clinical Research
ISSN : 2333-6676
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
Annals of Biometrics and Biostatistics
ISSN : 2374-0116
Launched : 2013
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
Author Information X