![International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 2, April 2018, pp. 1140~1149
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i2.pp1140-1149 1140
Journal homepage: http://iaescore.com/journals/index.php/IJECE
A Survey on Graph Database Management Techniques for
Huge Unstructured Data
Patil N. S.1
, Kiran P2
, Kavya N. P.3
, Naresh Patel K. M.4
1
Information Science & Engineering, BIET, Davangere, Karnataka, India
2
Computer Science & Engineering, RNSIT, Bengaluru, Karnataka, India
3
MCA Department, RNSIT, Bengaluru, Karnataka, India
4
Computer Science & Engineering, BIET, Davangere, Karnataka, India
Article Info ABSTRACT
Article history:
Received Nov 18, 2017
Revised Jan 1, 2018
Accepted Dec 11, 2018
Data analysis, data management, and big data play a major role in both social
and business perspective, in the last decade. Nowadays, the graph database is
the hottest and trending research topic. A graph database is preferred to deal
with the dynamic and complex relationships in connected data and offer
better results. Every data element is represented as a node. For example, in
social media site, a person is represented as a node, and its properties name,
age, likes, and dislikes, etc and the nodes are connected with the relationships
via edges. Use of graph database is expected to be beneficial in business, and
social networking sites that generate huge unstructured data as that Big Data
requires proper and efficient computational techniques to handle with. This
paper reviews the existing graph data computational techniques and the
research work, to offer the future research line up in graph database
management.
Keyword:
Big data
Dynamic schema
Graph database
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Patil N. S.,
Information Science & Engineering,
BIET,
Davangere, Karnataka, India.
Email: nsp4041@gmail.com
1. INTRODUCTION
Today, the user data is increasing rapidly due to many data generating processes like latest social
media networks, rapid adaptation of smartphones and handheld devices further enhances the data creation.
The computation of this data is becoming more difficult day by day, as the users of the digital data and
networks are increasing by manifolds [1]. Traditional databases cannot compute this huge data without
complexity for the real-time responses; whereas, in the case of graph databases, a graph is generated for each
entity, which speeds up the process. The use case for a graph database scenario is content-based data
filtering. Graph database provides better performance and data consistency; hence many researchers are
considering the graph models [2].
In order to handle the issues of storing huge data, many of the researchers have presented the
concept of graph and graph storage, in which the graphs are implied to model the huge data with complicated
design. In every graph, there will be nodes, properties, and edges as the relationship among them. The
connected data graph database also offers the significant choice to deal with the structured, semi-structure
and unstructured data [3]. The graph database offers the fastest response to a query, many times, in
milliseconds. Today, the graph databases are widely used in retail, social network, healthcare,
communication and other online solutions. Operations like create, update, read and delete are available in
graph database system. The drawback of these systems is that it is more expensive by nature than the
traditional methods [4].](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-1-320.jpg)
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This survey paper discusses the concepts of graph databases, review of the existing research
regarding existing computational techniques of data management. Section 2 discusses some basic conceptual
aspects of graph databases, modeling, computational techniques and comparisons of techniques. Section 3
provides the literature review of recent research work in graph database management, Graph database
computational techniques. Section 4 provides the research gap in recent research work of Graph Databases.
Section 5 describes the future research lineup, and finally, Section 6 concludes the paper.
2. GARAPH DATA
In recent years the way Internet and mobile communication have been used for different and varied
needs and applications by a common user, academicians, researchers have been started rethinking as for how
to store the huge data which is being generated every day, every hour and every minute. This need for the
storage and retrieval of data and information brought back the concepts of graph and graph models [4], [5].
Graphs are used to model complicated structures. The graph is a collection of nodes, edges, and the
relationships between them. In the graph, nodes are called entities, and there are many ways in which these
entities are co-related in a different type of applications. The connection between these entities is called as a
relationship. In graphs, data term “Attributes” related to entities and relationships are called labels. In a graph
like structure, data is stored into nodes, and these nodes have some properties. In graphs, relationships consist
of properties and connect one node to the other node.
The example shown in Figure 1, demonstrates the relationship between the two animals. In the
above figure, two things are identified that Thing-1 and Thing-2, exhibit properties like animal type, name
(cat & dog) and relationship. The representation says that the Thing-1 and Thing-2 are dog and cat
respectively and are named as cute and handsome respectively. Finally, both dog and cat relationship is
mentioned due to its animal category.
Type 1 Type 2
Cute Handsome
Relationship
Graph database
Creation
Reading
Updatation
Removing
Figure 1. Example of Graph data Figure 2. Units of graph database system
2.1. Graph Databases
The Graph database system has four different units such as creation, reading, updatation and
removing, which can be used in designing of graph data model. The Index free square matrix of finite graph
representation is more necessary to get the high-performance graph traversal. Graph database utilizes the
square matrix or adjacency then each node manages the direct relationship with the adjacent nodes. The graph
database exhibit a single data structure known as a graph, and it has no combined operation, and hence each
edge will be connected to another edge. The graph will store the data in nodes having a relationship. The data
in the graph database will follow the model graph property. Graph-oriented database is specialized No SQL,
where relationships among the nodes are stored and managed generically. Built-in support for relations makes
the traversal much faster for multidimensional, interconnected datasets; hence suitable for online transaction
processing (OLTP). For the same reason, organic and ground-up products like neo4j offer multifold
performance benefits in comparison to multilayer abstractions over the traditional technologies like relational
databases (RDB), and object-oriented databases (OODB). It also simplifies the complexity of design and](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-2-320.jpg)
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implementation; popular notation is “if you can see whiteboard, you can graph.” Being a high-level abstraction
to the network model database, it reduced the coding effort to one-tenth; it’s a key technology used in rapid
application development (RAD) [5], [6].
Graph databases are quickly making inroads into real life from research laboratories; many social
networking enterprises like Twitter, Facebook, and Google have already adopted years ago. Recently the
technology- not only the scientific data but also the web and many different kinds of data can be modeled as a
graph. This helps to overcome the limitations of RDBMS, like predefined schema and to process complex
queries in milliseconds. Especially, lack of schema allows developers to gain high productivity, besides
providing the capability to process complex multilevel queries in real-time. E-commerce sites and users
benefit from the easy processing of the recommended product. Machine learning algorithms are utilized the
most, for the applications such as these where big data analytics is used by global top 100 companies. Bug
Localization is another application area worth mentioning for the use of graph databases. Overall there is a
variety of domains where graph data modeling can be applied to revolutionize the user experience [7].
2.2. Existing types of Graph Database Models
In recent past, many tools are developed using the graph database concept, for example, Neo4J and
Sparksee [8]. The tools like Oracle spatial and graph, OQGraph, and ArangoDB are designed as an abstraction
with the underlying architecture of relational databases MySql, Oracle [9]. Until now, there is no industry
standard [10], and moreover many of them are designed to be suited for a particular domain [11]. In the case of
In-memory model, scalability is limited as the memory holds the content [12]. Another reason for inefficiency
into the model is due to horizontal scaling and layering mechanism. The requirement of the new paradigm is
for handling an extensive data; very few models are designed to adopt parallelism as well OLTP. The early
models lack standard query language, Application programming interface, and protocols as found in
conventional models such as SQL, JDBC, and REST. Lately, Gremlin and SPARQL are gaining consensus,
but the adaptation is too slow.
2.3. Neo4j (Neo Technology)
Neo4j is a disk-based transactional graph database and named as “World leading graph database.” Its
first release date was in 2007. Neo4j also supports another language like Python except for Java for graph
operations. Neo4j is an open source project [7] available in a GPLv3 Community edition, with Advanced and
Enterprise editions available under both the AGPLv3 as well as a commercial license. Neo4j is best graph
database for enterprise deployment. It scales to billions of nodes and relationships in a network. Neo4j
manages all the operations that modify data in a transaction. In Neo4j both nodes and relationship can contain
properties. Neo4j is a graph database that manages graphs and is optimized for graph structure instead of
tables. It is the more expressive type of graph database is similar to other graph databases. Neo4j is most
popular graph databases today [8].
2.4. Hyper Graph DB
It is an open-source database supports hype graphs. Hyper graph [8] is different from the normal
graph because in this edge is points to the other edges. In various fields, it is used in the modeling of the graph
data. It supports online querying with an API written in Java. It is based on the Hyper Graph DB model. It is a
universal data model highly complex and large-scale knowledge application. It has graph-oriented storage and
customizable indexing. In this graph database, a hyper edge is easy to convert into a tuple. It is a distributed
and graph-oriented database [8-14].
2.5. DEX
DEX [15] is said to be very efficient and bitmaps-based graph database and is written in C++
language. It was first released in 2008. It makes graph querying possible in different networks like social
network analysis and pattern recognition. It is also known as high-performance graph database in the case of
large graphs and useful for most of the NoSQL applications. The latest version of DEX supports both Java
and.NET programming. It’s portable and requires only a single JAR file for execution. DEX is called the
fourth most popular graph database today [3], [6].
2.6. Trinity
Trinity is a distributed graph system [9] over a memory cloud. Memory Cloud is globally addressable
in memory key-value store over a cluster of the machine. It provides fast data access power when we have
large datasets. It is a large graph processing machine. It provides fast graph exploration and parallel computing
for larger datasets. It also provides high throughput on large graphs which have a billion nodes.](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-3-320.jpg)
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2.7. Infinite Graph (Objectivity)
Infinite Graph is produced by an organization called Objectivity. It is a type of company that works to
develops database technologies supporting large-scale, object persistence and relationship analytics. An
infinite graph database is a distributed graph database in Java, and it is based on a graph like structure. We can
call infinite graph as a cloud-enabled graph database. It is designed for to handle the very high throughput. It is
a single graph database distributed across multiple machines. There is a lock server which handles lock
requests from database applications. It is capable of dealing with complex relationship requiring multiple hops.
It provides graph-wise indexes on multiple key fields and also provides high performance regarding
query [7], [10].
2.8. Titan
Titan [9] was adopted in 2012. It is written in Java and an open source project. The main benefit of
using Titan is its scaling feature. It also provides support to very large graphs and scales with the number of
machines in a cluster. It is also highly scalable graph database regarding concurrent users and size of the
graph. It provides a batch graph processing with Hadoop framework and also gives answers to complex
queries in milliseconds. It consists of three main components:
a. Native Blueprints Implementation
b. Gremlin Query language
c. Rexster Server
It follows property graph model and supports Gremlin: a graph traversal query language. It also offers an
optimized disk representation for efficient use of storage and speed of accessing data. Applications can interact
with Titan in mainly two ways:
a. First Method is that calls Java-language API’s related to Titan which includes its native API
implementation.
b. TinkerPop stack utilities such as Gremlin query language built atop Blueprints.
Recent research survey
The research in the domain of graph data is classified into ten different categories by considering IEEE
Xplore journals. The categorization is given as below.
2.9. Data Store Efficiency
In order to bring the better efficiency of the data storage, some of the issues may exist among these
data compression is necessary to store more data. Also, the data standardization may play a greater role to map
the data and translate for cloud storage. In the large, the super graph search is required to choose the data
graphs features. The recent work done in this category Table 1 below.
Table 1. Work for data store efficiency
Author Issue considered Method adopted Result
Sutrisna et al. [4] Data compression Graph clustering algorithm with data
set of collaboration data among journal
writers
Lossless compressed graph data is
achieved
Bansal et al. [5] Data standardization and
classification
online compression algorithm Reduction in number of nodes and
achieved graph database
compression for less storage space
Lyu et al. [6] Super graph search indexing and query processing
algorithms , CCD dataset and NCI
dataset
Indexing and processing time
Chen and Chen
[7]
Supporting reach ability
queries
Decomposition of graphs High efficiency, effectiveness and
querying time
2.10. Database Indexing Method
In order to facilitate isomorphism and similarity queries and building efficient graph database systems
and accelerate graph similarity search, much significant works is performed. The following Table 2 is the
some of the chosen work in database indexing method.](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-4-320.jpg)
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Table 2. Work for database indexing method
Author Issue considered Method adopted Result
Williams et al. [8] Graph storage, similarity
and indexing
Sub-graph isomorphism and similarity queries
mechanism by using Protein motifs and synthetic
datasets
Achieved improved query
times for sub-graph
isomorphism queries.
Luo et al. [9] Large scale graph
database indexing and
search approach
T-mixture model (Combination of optimized vector
quantizer and probabilistic approximate ate based
indexing scheme)
Robust in handling outliers.
Yuan et al. [10] Graph feature mining Query grouping mechanism Achieved better , faster and
light weight filtering,
Bei et al. [11] Graph search Distributed graph searching mechanism Achieved distributed graph
database
Goldberg et al.
[12]
Problem of fragment
identification
Heuristic mechanism Achieves optimized running
time
2.11. Graph Indexing Method
The improvisation of graph data modeling was done for different data. The following Table 3 briefs
the work performed for graph data modeling and graph based management system.
Table 3. Work for graph indexing method
Author Issue considered Method adopted Result
Dongoran et al. [13] Data modeling Index construction, database
filtering, sub-graph matching
Achieved more path length, more
indexing time
Kang et al. [14] Dynamic graphs storage and
manage
Graph based database
management system
Robust in handling outliers.
2.12. Sub-graph matching method
This part briefs some research ideas presented by many researchers in data querying, sub-graph
matching, etc. The recent works for better sub-graph matching are presented. Also, the works shown in the
following Table 4 gives the ideas about various graph data techniques.
Table 4. Work for sub-graph matching method
Author Issue considered Method adopted Result
Giugno Shasha [15] Graph querying Regular expression graph query language
that combines Xpath and Smart; hash-
based finger-printing
performs well for small query
graphs on large graph databases
(in the thousands
Bröcheler et al. [16] Sub-graph Matching probabilistic method to estimate
probabilities; Partition algorithm for
creating index
Works efficiently, answering
778M edge real-world SN in
under one second.
Bröcheler et al. [17] Approximate sub-graph
Matching
PMATCH algorithm Efficient and scales to over a
billion edges.
Bröcheler et al. [18] Sub-graph Matching; long-
tailed degree distributions
delicious social book-marking service Faster than static cost models for
warm caches.
Hong et al. [19] Set similarity Set similarity pruning and structure-
based pruning; dominating-set-based
sub-graph matching; inverted pattern
lattice and structural signature buckets
are designed
outperforms state-of-the-art
methods by an order of magnitude
Hoksza and Jelínek
[20]
protein-protein interface
(PPI) identification
knowledge-based approach Using Neo4j
for mining protein graphs
in comparison to Microsoft SQL
Server, Neo4j is a viable option
for small, sub-graph query types
2.13. Semantic
The recent works that are addressed the semantic approach, Query semantic data processing; data
analysis in a graph database is given Table 5.](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-5-320.jpg)
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Table 5. Work for graph database semantic
Kivikangas and
Ishizuka [21]
Semantic Queries Utilizing Universal Words (UWs).
Concept Description Language (CDL)
for semantic data instead of RDF, and
Neo4j
Improved Semantic Queries
Kalmegh and
Navathe [22]
high-performance graph
databases
Survey Define,
Characteristics,
Future directions
Irawan and
Prihatmanto [23]
Implementation of Graph
Database for OpenCog
GraphBacking Store API extends
Backing Store C++ API
Atom Space represents knowledge
in a hyper graph structure, persisting
in a graph database is more intuitive
and more portable
Graves et al. [24] Design of data store for
genome
Review all available options and
compare
Graph database
Cesare et al. [25] Automated taxonomy
extraction from semantic
process models
linguistic approach based on semantic
similarity
generalize elements of business
processes that are automatically
discovered from semi-structured
data projects
Morari et al. [26] Scaling Semantic Graph
Databases
SPARQLTO-C++ COMPILER, A
Library Of Distributed Data system,
And A Custom Multithreaded Runtime
Better scaling
Wardani and Kiing
[27]
Semantic Mapping
Relational to Graph Model
create property relationship in the result
of the mapping and converting process
map and convert the relational data
model to graph model without
semantic loss
Souza et al. [28] Graph Databases with
Semantic Network Models
for Software-Defined
Networking (SDN)
Applications
Import the Network Markup Language
(NML) model
modeling tasks are considerably
more natural compared to RDBM; to
reproduce SDN application
primitives
Hayakawa and
Nishiyama [29]
Query Processing of
Semantic Data
Summarized graph in advance improve the query performance by
6.62 times
Bednar et al. [30] theoretical analysis and
performance testing
Benchmark data modeling and
population
Comparative benefits& weakness
Hartley et al. [31] storing,
Accessing and analyzing
Trillion vertices and edges.
semantic graphs execution of parallel
out-of-core graph algorithms
outperforms widely used open-
source
Cavoto et al. [32] Network-Driven Data
Analysis
FishBase global information system discovery of new information and
validating the existing data
Caldarola et al. [33] Big Graph-based Data
Visualization
The WordNet efficiency, effectiveness and
clearness; two different
representation of Word-
Net
Lamhaddab and
Elbaamrani [34]
Graph Modeling
For Mobiles
implementation of
an extractor module (in java language)
Reverse engineering from iOS
platform to Android platform
Leida and. Chu [35] Distributed SPARQL query
answering over RDF
data streams
Business Process
Monitoring domain for Query workload
balancing
Approach for efficient and scalable
query processing over RDF graphs
distributed over a local data grid.
Mordinyi et al. [36] efficient data store that is
capable of versioning and
querying local and common
concepts
NoSQL graph database outperforms ontology stores and
match solutions relying on relational
databases
Balboni et al. [37] Evolution Analysis natural language processing engines to
build temporal graph database
Got large amount of open source
documents
Wu and Chen [38] Frequent Sub-graph Mining By
normalizing the incidence matrix
Achieved higher speed and
efficiency
John et al. [39] Learning process
enhancement against
population
Natural
Language Processing
enhanced learner centered online
learning experience
Xu and Luo [40] Expression-Driven Sketch
Graph Matching for Face
Recognition
multi-layer grammatical face model recognition rates were improved,
especially for the smiling and
screaming faces whose line-edge
maps are greatly distorted
Figueira and Libkin
[41]
Querying Graphs Parikh automata real-life querying
2.14. Social Networking
The recent ideas towards the graph data generated by social networks are presented in the following
Table 6.](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-6-320.jpg)
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Table 6. Work for social networking graph database
Author Issue considered Method adopted Result
Dayarathna and
Suzumura [42]
Graph Database for Hybrid Clouds Distributed Graph Database Achieves faster performance
Soussi [43] Social Network Extraction Graph Database Achieves better extraction
Yar and Tun [44] Searching Personnel Relationship Graph Database relationship among persons can
simply and accurately be inferred
Mir and Wright [45] Differentially Private Estimator Kronecker graph model Generate synthetic graphs that are
“similar” to the original target
graphs in a privacy preserving
manner.
Shrivastava and Pal
[46]
Graph Mining Graph data processing,
extraction and visualization
Achieved quality in processing
2 RESEARCH GAP IN THE EXISTING WORK
The research work that performed in recent years is lacking with effectiveness in a huge
unstructured graph database. The work of the various authors lacks the research efficiency in their methods.
The applicability of these methods in real time applications shows poor performance. There is a need for
proper research in a huge an unstructured graph database management. The following are the listed research
gaps in the existing work:
a. The existing researches are doesn’t support the huge unstructured graph data induces complexity
of technique and some of the performance metrics are need to be explored in graph data.
b. The existing researches doesn’t fulfill the challenges of the hosting graph database like dynamic
nature of graph database volume, tough to maintain the graph data and consumes higher
computational time for evaluation of graph queries.
c. The graph has partitioning issue, information loss and unnecessary computation are still
unaddressed and are need to address for unstructured graph database.
d. The researches which are mentioned in past are not designed for data mining of graph database.
e. The existing privacy preservation techniques using data anonymity approaches are not efficient
as this approach doesn’t provide theoretical evidence that the provided solution is effective
against the security issues.
3 LINE OF RESEARCH IN FUTURE
The better graph database scalability and management of huge unstructured graph database can be attained as
below steps.
a. Outlining the existing issues in current work.
b. A prototype is needs to be designed to generate large and unstructured data for real-time
applications and also implement the respective graph theory to model the graph database.
c. A novel data mining algorithm is needed to be developed for the graph database having low
computational complexity.
d. A cost effective mechanism is needed to be build, and it should offer privacy for huge
unstructured data.
e. The effectiveness of the mechanism is needed to tally with the existing work.
4 CONCLUSION
This survey paper gives the better idea of required attributes to manage the huge unstructured graph
database. This paper gives the different types graph database like Neo4j, DEX, and Titan, etc. The survey of
recent work towards graph data is collected studied and represented in various sections like Data store
efficiency, Database Indexing Method, Graph Indexing Method, Sub-graph matching method, Semantic and
social networking graph database. From the above existing research survey, a research gap is defined, and
flowingly the research ideas for improving the graph database are presented.
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BIOGRAPIES OF AUTHORS
Patil N S, working as Assistant Professor Department of Information Science, BIET, Davangere.
My teaching experience is 7yrs area of interest Database management system, Datamining, Graph
Datamining, Big Data.](https://image.slidesharecdn.com/v561jan1829dec1718nov10417-13564-1-ed-201110085143/85/A-Survey-on-Graph-Database-Management-Techniques-for-Huge-Unstructured-Data-9-320.jpg)
A Survey on Graph Database Management Techniques for Huge Unstructured Data
- 1. International Journal of Electrical and Computer Engineering (IJECE) Vol. 8, No. 2, April 2018, pp. 1140~1149 ISSN: 2088-8708, DOI: 10.11591/ijece.v8i2.pp1140-1149 1140 Journal homepage: http://iaescore.com/journals/index.php/IJECE A Survey on Graph Database Management Techniques for Huge Unstructured Data Patil N. S.1 , Kiran P2 , Kavya N. P.3 , Naresh Patel K. M.4 1 Information Science & Engineering, BIET, Davangere, Karnataka, India 2 Computer Science & Engineering, RNSIT, Bengaluru, Karnataka, India 3 MCA Department, RNSIT, Bengaluru, Karnataka, India 4 Computer Science & Engineering, BIET, Davangere, Karnataka, India Article Info ABSTRACT Article history: Received Nov 18, 2017 Revised Jan 1, 2018 Accepted Dec 11, 2018 Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management. Keyword: Big data Dynamic schema Graph database Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Patil N. S., Information Science & Engineering, BIET, Davangere, Karnataka, India. Email: [email protected] 1. INTRODUCTION Today, the user data is increasing rapidly due to many data generating processes like latest social media networks, rapid adaptation of smartphones and handheld devices further enhances the data creation. The computation of this data is becoming more difficult day by day, as the users of the digital data and networks are increasing by manifolds [1]. Traditional databases cannot compute this huge data without complexity for the real-time responses; whereas, in the case of graph databases, a graph is generated for each entity, which speeds up the process. The use case for a graph database scenario is content-based data filtering. Graph database provides better performance and data consistency; hence many researchers are considering the graph models [2]. In order to handle the issues of storing huge data, many of the researchers have presented the concept of graph and graph storage, in which the graphs are implied to model the huge data with complicated design. In every graph, there will be nodes, properties, and edges as the relationship among them. The connected data graph database also offers the significant choice to deal with the structured, semi-structure and unstructured data [3]. The graph database offers the fastest response to a query, many times, in milliseconds. Today, the graph databases are widely used in retail, social network, healthcare, communication and other online solutions. Operations like create, update, read and delete are available in graph database system. The drawback of these systems is that it is more expensive by nature than the traditional methods [4].
- 2. Int J Elec & Comp Eng ISSN: 2088-8708 A Survey on Graph Database Management Techniques for Huge Unstructured Data (Patil N. S.) 1141 This survey paper discusses the concepts of graph databases, review of the existing research regarding existing computational techniques of data management. Section 2 discusses some basic conceptual aspects of graph databases, modeling, computational techniques and comparisons of techniques. Section 3 provides the literature review of recent research work in graph database management, Graph database computational techniques. Section 4 provides the research gap in recent research work of Graph Databases. Section 5 describes the future research lineup, and finally, Section 6 concludes the paper. 2. GARAPH DATA In recent years the way Internet and mobile communication have been used for different and varied needs and applications by a common user, academicians, researchers have been started rethinking as for how to store the huge data which is being generated every day, every hour and every minute. This need for the storage and retrieval of data and information brought back the concepts of graph and graph models [4], [5]. Graphs are used to model complicated structures. The graph is a collection of nodes, edges, and the relationships between them. In the graph, nodes are called entities, and there are many ways in which these entities are co-related in a different type of applications. The connection between these entities is called as a relationship. In graphs, data term “Attributes” related to entities and relationships are called labels. In a graph like structure, data is stored into nodes, and these nodes have some properties. In graphs, relationships consist of properties and connect one node to the other node. The example shown in Figure 1, demonstrates the relationship between the two animals. In the above figure, two things are identified that Thing-1 and Thing-2, exhibit properties like animal type, name (cat & dog) and relationship. The representation says that the Thing-1 and Thing-2 are dog and cat respectively and are named as cute and handsome respectively. Finally, both dog and cat relationship is mentioned due to its animal category. Type 1 Type 2 Cute Handsome Relationship Graph database Creation Reading Updatation Removing Figure 1. Example of Graph data Figure 2. Units of graph database system 2.1. Graph Databases The Graph database system has four different units such as creation, reading, updatation and removing, which can be used in designing of graph data model. The Index free square matrix of finite graph representation is more necessary to get the high-performance graph traversal. Graph database utilizes the square matrix or adjacency then each node manages the direct relationship with the adjacent nodes. The graph database exhibit a single data structure known as a graph, and it has no combined operation, and hence each edge will be connected to another edge. The graph will store the data in nodes having a relationship. The data in the graph database will follow the model graph property. Graph-oriented database is specialized No SQL, where relationships among the nodes are stored and managed generically. Built-in support for relations makes the traversal much faster for multidimensional, interconnected datasets; hence suitable for online transaction processing (OLTP). For the same reason, organic and ground-up products like neo4j offer multifold performance benefits in comparison to multilayer abstractions over the traditional technologies like relational databases (RDB), and object-oriented databases (OODB). It also simplifies the complexity of design and
- 3. ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 8, No. 2, April 2018 : 1140 – 1149 1142 implementation; popular notation is “if you can see whiteboard, you can graph.” Being a high-level abstraction to the network model database, it reduced the coding effort to one-tenth; it’s a key technology used in rapid application development (RAD) [5], [6]. Graph databases are quickly making inroads into real life from research laboratories; many social networking enterprises like Twitter, Facebook, and Google have already adopted years ago. Recently the technology- not only the scientific data but also the web and many different kinds of data can be modeled as a graph. This helps to overcome the limitations of RDBMS, like predefined schema and to process complex queries in milliseconds. Especially, lack of schema allows developers to gain high productivity, besides providing the capability to process complex multilevel queries in real-time. E-commerce sites and users benefit from the easy processing of the recommended product. Machine learning algorithms are utilized the most, for the applications such as these where big data analytics is used by global top 100 companies. Bug Localization is another application area worth mentioning for the use of graph databases. Overall there is a variety of domains where graph data modeling can be applied to revolutionize the user experience [7]. 2.2. Existing types of Graph Database Models In recent past, many tools are developed using the graph database concept, for example, Neo4J and Sparksee [8]. The tools like Oracle spatial and graph, OQGraph, and ArangoDB are designed as an abstraction with the underlying architecture of relational databases MySql, Oracle [9]. Until now, there is no industry standard [10], and moreover many of them are designed to be suited for a particular domain [11]. In the case of In-memory model, scalability is limited as the memory holds the content [12]. Another reason for inefficiency into the model is due to horizontal scaling and layering mechanism. The requirement of the new paradigm is for handling an extensive data; very few models are designed to adopt parallelism as well OLTP. The early models lack standard query language, Application programming interface, and protocols as found in conventional models such as SQL, JDBC, and REST. Lately, Gremlin and SPARQL are gaining consensus, but the adaptation is too slow. 2.3. Neo4j (Neo Technology) Neo4j is a disk-based transactional graph database and named as “World leading graph database.” Its first release date was in 2007. Neo4j also supports another language like Python except for Java for graph operations. Neo4j is an open source project [7] available in a GPLv3 Community edition, with Advanced and Enterprise editions available under both the AGPLv3 as well as a commercial license. Neo4j is best graph database for enterprise deployment. It scales to billions of nodes and relationships in a network. Neo4j manages all the operations that modify data in a transaction. In Neo4j both nodes and relationship can contain properties. Neo4j is a graph database that manages graphs and is optimized for graph structure instead of tables. It is the more expressive type of graph database is similar to other graph databases. Neo4j is most popular graph databases today [8]. 2.4. Hyper Graph DB It is an open-source database supports hype graphs. Hyper graph [8] is different from the normal graph because in this edge is points to the other edges. In various fields, it is used in the modeling of the graph data. It supports online querying with an API written in Java. It is based on the Hyper Graph DB model. It is a universal data model highly complex and large-scale knowledge application. It has graph-oriented storage and customizable indexing. In this graph database, a hyper edge is easy to convert into a tuple. It is a distributed and graph-oriented database [8-14]. 2.5. DEX DEX [15] is said to be very efficient and bitmaps-based graph database and is written in C++ language. It was first released in 2008. It makes graph querying possible in different networks like social network analysis and pattern recognition. It is also known as high-performance graph database in the case of large graphs and useful for most of the NoSQL applications. The latest version of DEX supports both Java and.NET programming. It’s portable and requires only a single JAR file for execution. DEX is called the fourth most popular graph database today [3], [6]. 2.6. Trinity Trinity is a distributed graph system [9] over a memory cloud. Memory Cloud is globally addressable in memory key-value store over a cluster of the machine. It provides fast data access power when we have large datasets. It is a large graph processing machine. It provides fast graph exploration and parallel computing for larger datasets. It also provides high throughput on large graphs which have a billion nodes.
- 4. Int J Elec & Comp Eng ISSN: 2088-8708 A Survey on Graph Database Management Techniques for Huge Unstructured Data (Patil N. S.) 1143 2.7. Infinite Graph (Objectivity) Infinite Graph is produced by an organization called Objectivity. It is a type of company that works to develops database technologies supporting large-scale, object persistence and relationship analytics. An infinite graph database is a distributed graph database in Java, and it is based on a graph like structure. We can call infinite graph as a cloud-enabled graph database. It is designed for to handle the very high throughput. It is a single graph database distributed across multiple machines. There is a lock server which handles lock requests from database applications. It is capable of dealing with complex relationship requiring multiple hops. It provides graph-wise indexes on multiple key fields and also provides high performance regarding query [7], [10]. 2.8. Titan Titan [9] was adopted in 2012. It is written in Java and an open source project. The main benefit of using Titan is its scaling feature. It also provides support to very large graphs and scales with the number of machines in a cluster. It is also highly scalable graph database regarding concurrent users and size of the graph. It provides a batch graph processing with Hadoop framework and also gives answers to complex queries in milliseconds. It consists of three main components: a. Native Blueprints Implementation b. Gremlin Query language c. Rexster Server It follows property graph model and supports Gremlin: a graph traversal query language. It also offers an optimized disk representation for efficient use of storage and speed of accessing data. Applications can interact with Titan in mainly two ways: a. First Method is that calls Java-language API’s related to Titan which includes its native API implementation. b. TinkerPop stack utilities such as Gremlin query language built atop Blueprints. Recent research survey The research in the domain of graph data is classified into ten different categories by considering IEEE Xplore journals. The categorization is given as below. 2.9. Data Store Efficiency In order to bring the better efficiency of the data storage, some of the issues may exist among these data compression is necessary to store more data. Also, the data standardization may play a greater role to map the data and translate for cloud storage. In the large, the super graph search is required to choose the data graphs features. The recent work done in this category Table 1 below. Table 1. Work for data store efficiency Author Issue considered Method adopted Result Sutrisna et al. [4] Data compression Graph clustering algorithm with data set of collaboration data among journal writers Lossless compressed graph data is achieved Bansal et al. [5] Data standardization and classification online compression algorithm Reduction in number of nodes and achieved graph database compression for less storage space Lyu et al. [6] Super graph search indexing and query processing algorithms , CCD dataset and NCI dataset Indexing and processing time Chen and Chen [7] Supporting reach ability queries Decomposition of graphs High efficiency, effectiveness and querying time 2.10. Database Indexing Method In order to facilitate isomorphism and similarity queries and building efficient graph database systems and accelerate graph similarity search, much significant works is performed. The following Table 2 is the some of the chosen work in database indexing method.
- 5. ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 8, No. 2, April 2018 : 1140 – 1149 1144 Table 2. Work for database indexing method Author Issue considered Method adopted Result Williams et al. [8] Graph storage, similarity and indexing Sub-graph isomorphism and similarity queries mechanism by using Protein motifs and synthetic datasets Achieved improved query times for sub-graph isomorphism queries. Luo et al. [9] Large scale graph database indexing and search approach T-mixture model (Combination of optimized vector quantizer and probabilistic approximate ate based indexing scheme) Robust in handling outliers. Yuan et al. [10] Graph feature mining Query grouping mechanism Achieved better , faster and light weight filtering, Bei et al. [11] Graph search Distributed graph searching mechanism Achieved distributed graph database Goldberg et al. [12] Problem of fragment identification Heuristic mechanism Achieves optimized running time 2.11. Graph Indexing Method The improvisation of graph data modeling was done for different data. The following Table 3 briefs the work performed for graph data modeling and graph based management system. Table 3. Work for graph indexing method Author Issue considered Method adopted Result Dongoran et al. [13] Data modeling Index construction, database filtering, sub-graph matching Achieved more path length, more indexing time Kang et al. [14] Dynamic graphs storage and manage Graph based database management system Robust in handling outliers. 2.12. Sub-graph matching method This part briefs some research ideas presented by many researchers in data querying, sub-graph matching, etc. The recent works for better sub-graph matching are presented. Also, the works shown in the following Table 4 gives the ideas about various graph data techniques. Table 4. Work for sub-graph matching method Author Issue considered Method adopted Result Giugno Shasha [15] Graph querying Regular expression graph query language that combines Xpath and Smart; hash- based finger-printing performs well for small query graphs on large graph databases (in the thousands Bröcheler et al. [16] Sub-graph Matching probabilistic method to estimate probabilities; Partition algorithm for creating index Works efficiently, answering 778M edge real-world SN in under one second. Bröcheler et al. [17] Approximate sub-graph Matching PMATCH algorithm Efficient and scales to over a billion edges. Bröcheler et al. [18] Sub-graph Matching; long- tailed degree distributions delicious social book-marking service Faster than static cost models for warm caches. Hong et al. [19] Set similarity Set similarity pruning and structure- based pruning; dominating-set-based sub-graph matching; inverted pattern lattice and structural signature buckets are designed outperforms state-of-the-art methods by an order of magnitude Hoksza and Jelínek [20] protein-protein interface (PPI) identification knowledge-based approach Using Neo4j for mining protein graphs in comparison to Microsoft SQL Server, Neo4j is a viable option for small, sub-graph query types 2.13. Semantic The recent works that are addressed the semantic approach, Query semantic data processing; data analysis in a graph database is given Table 5.
- 6. Int J Elec & Comp Eng ISSN: 2088-8708 A Survey on Graph Database Management Techniques for Huge Unstructured Data (Patil N. S.) 1145 Table 5. Work for graph database semantic Kivikangas and Ishizuka [21] Semantic Queries Utilizing Universal Words (UWs). Concept Description Language (CDL) for semantic data instead of RDF, and Neo4j Improved Semantic Queries Kalmegh and Navathe [22] high-performance graph databases Survey Define, Characteristics, Future directions Irawan and Prihatmanto [23] Implementation of Graph Database for OpenCog GraphBacking Store API extends Backing Store C++ API Atom Space represents knowledge in a hyper graph structure, persisting in a graph database is more intuitive and more portable Graves et al. [24] Design of data store for genome Review all available options and compare Graph database Cesare et al. [25] Automated taxonomy extraction from semantic process models linguistic approach based on semantic similarity generalize elements of business processes that are automatically discovered from semi-structured data projects Morari et al. [26] Scaling Semantic Graph Databases SPARQLTO-C++ COMPILER, A Library Of Distributed Data system, And A Custom Multithreaded Runtime Better scaling Wardani and Kiing [27] Semantic Mapping Relational to Graph Model create property relationship in the result of the mapping and converting process map and convert the relational data model to graph model without semantic loss Souza et al. [28] Graph Databases with Semantic Network Models for Software-Defined Networking (SDN) Applications Import the Network Markup Language (NML) model modeling tasks are considerably more natural compared to RDBM; to reproduce SDN application primitives Hayakawa and Nishiyama [29] Query Processing of Semantic Data Summarized graph in advance improve the query performance by 6.62 times Bednar et al. [30] theoretical analysis and performance testing Benchmark data modeling and population Comparative benefits& weakness Hartley et al. [31] storing, Accessing and analyzing Trillion vertices and edges. semantic graphs execution of parallel out-of-core graph algorithms outperforms widely used open- source Cavoto et al. [32] Network-Driven Data Analysis FishBase global information system discovery of new information and validating the existing data Caldarola et al. [33] Big Graph-based Data Visualization The WordNet efficiency, effectiveness and clearness; two different representation of Word- Net Lamhaddab and Elbaamrani [34] Graph Modeling For Mobiles implementation of an extractor module (in java language) Reverse engineering from iOS platform to Android platform Leida and. Chu [35] Distributed SPARQL query answering over RDF data streams Business Process Monitoring domain for Query workload balancing Approach for efficient and scalable query processing over RDF graphs distributed over a local data grid. Mordinyi et al. [36] efficient data store that is capable of versioning and querying local and common concepts NoSQL graph database outperforms ontology stores and match solutions relying on relational databases Balboni et al. [37] Evolution Analysis natural language processing engines to build temporal graph database Got large amount of open source documents Wu and Chen [38] Frequent Sub-graph Mining By normalizing the incidence matrix Achieved higher speed and efficiency John et al. [39] Learning process enhancement against population Natural Language Processing enhanced learner centered online learning experience Xu and Luo [40] Expression-Driven Sketch Graph Matching for Face Recognition multi-layer grammatical face model recognition rates were improved, especially for the smiling and screaming faces whose line-edge maps are greatly distorted Figueira and Libkin [41] Querying Graphs Parikh automata real-life querying 2.14. Social Networking The recent ideas towards the graph data generated by social networks are presented in the following Table 6.
- 7. ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 8, No. 2, April 2018 : 1140 – 1149 1146 Table 6. Work for social networking graph database Author Issue considered Method adopted Result Dayarathna and Suzumura [42] Graph Database for Hybrid Clouds Distributed Graph Database Achieves faster performance Soussi [43] Social Network Extraction Graph Database Achieves better extraction Yar and Tun [44] Searching Personnel Relationship Graph Database relationship among persons can simply and accurately be inferred Mir and Wright [45] Differentially Private Estimator Kronecker graph model Generate synthetic graphs that are “similar” to the original target graphs in a privacy preserving manner. Shrivastava and Pal [46] Graph Mining Graph data processing, extraction and visualization Achieved quality in processing 2 RESEARCH GAP IN THE EXISTING WORK The research work that performed in recent years is lacking with effectiveness in a huge unstructured graph database. The work of the various authors lacks the research efficiency in their methods. The applicability of these methods in real time applications shows poor performance. There is a need for proper research in a huge an unstructured graph database management. The following are the listed research gaps in the existing work: a. The existing researches are doesn’t support the huge unstructured graph data induces complexity of technique and some of the performance metrics are need to be explored in graph data. b. The existing researches doesn’t fulfill the challenges of the hosting graph database like dynamic nature of graph database volume, tough to maintain the graph data and consumes higher computational time for evaluation of graph queries. c. The graph has partitioning issue, information loss and unnecessary computation are still unaddressed and are need to address for unstructured graph database. d. The researches which are mentioned in past are not designed for data mining of graph database. e. The existing privacy preservation techniques using data anonymity approaches are not efficient as this approach doesn’t provide theoretical evidence that the provided solution is effective against the security issues. 3 LINE OF RESEARCH IN FUTURE The better graph database scalability and management of huge unstructured graph database can be attained as below steps. a. Outlining the existing issues in current work. b. A prototype is needs to be designed to generate large and unstructured data for real-time applications and also implement the respective graph theory to model the graph database. c. A novel data mining algorithm is needed to be developed for the graph database having low computational complexity. d. A cost effective mechanism is needed to be build, and it should offer privacy for huge unstructured data. e. The effectiveness of the mechanism is needed to tally with the existing work. 4 CONCLUSION This survey paper gives the better idea of required attributes to manage the huge unstructured graph database. This paper gives the different types graph database like Neo4j, DEX, and Titan, etc. The survey of recent work towards graph data is collected studied and represented in various sections like Data store efficiency, Database Indexing Method, Graph Indexing Method, Sub-graph matching method, Semantic and social networking graph database. From the above existing research survey, a research gap is defined, and flowingly the research ideas for improving the graph database are presented. REFERENCES [1] Nirwansyah, Ferdy, and Suharjito Suharjito, "Hybrid Disk Drive Configuration on Database Server Virtualization," Indonesian Journal of Electrical Engineering and Computer Science 2.3 (2016): 720-728. [2] Ismaeel, Salam, Ayman Al-Khazraji, and Karama Al-delimi, "Fuzzy Information Modeling in a Database System," International Journal of Artificial Intelligence (IJ-AI) 6.1 (2017): 1-7.
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- 10. Int J Elec & Comp Eng ISSN: 2088-8708 A Survey on Graph Database Management Techniques for Huge Unstructured Data (Patil N. S.) 1149 Dr Kiran P, working as Associate Professor Department Computer science, R. N. S Institute of Technology, Bangalore. His teaching experience 12yrs Area of interest Cryptography, Randomization methods & Anonymization methods in generalization, Indexing techniques and Design patterns. He has published 12 papers.