Semantic Computing for Big Data: Approaches, Tools, and Emerging Directions (2011-2014)

Abstract

The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance today's big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data.

1. Introduction

The term “big data” was coined to represent the large amount and many types of digital data that we use today, including documents, images, videos, audio, and websites. Semantics-based approaches are considered useful means of dealing with very large-scale data such as big data. In order to explore this topic, it is first necessary to more clearly describe the concept of big data.

1.1 Big Data

Although the term “big data” has not yet been defined by IEEE and it is not included in the online IEEE dictionary [8]. However, a number of definitions are presented in other popular sources, such as the following:

Generally, big data has three main characteristics: volume, velocity, and variety. However, [1] adds one more characteristic: value. We agree with the use of the value characteristic, and include it in our body of relevant and valuable information.

It has become obvious that new capabilities and technologies are needed to capture and analyze big data. The McKinsey Global Institute estimates that data volume is growing by 40% per year, and will grow to 44 times its initial size between 2009 and 2020. According to [1] the volume of data is not the only characteristic that matters. In fact, there are four key characteristics that define big data (Fig. 1):

Fig. 1.Four key characteristics of big data

Oracle classifies value as an essential characteristic for processing big data, which we partially agree with. In our opinion, value should not just be considered to involve economic value, but also include the meaningful information hidden in big data.

1.2 Significance of Big Data

The significance of big data is clear. The following statistics show the predicted data growth trends, in terms of volume.

Fig. 2 (a).Universal data growth

Data growth - IDC believes that the digital universe will grow by 44 times from 2009 to 2020. IBM estimates that data and content is growing at a compound annual growth rate of 64% a year or more (1 zettabyte = 1 trillion gigabytes). Source: IDC Digital Universe Study.

Fig. 2 (b).Data growth in China

Fig. 2(c).Data growth in India

Fig. 2 (d).Data growth in US

Fig. 2 (e).Data growth in Western Europe

 

2. Significance of Semantic Computing

The term “semantic web” has been around for more than a decade. Its origins trace back to a 2001 Scientific American article by Tim Berners-Lee, who is known as the inventor of the world wide web, and co-authors James Hendler and Ora Lassila [25]. The article presents a futuristic view of the web, where data is linked in a meaningful fashion. To realize this concept, two main streams have emerged: ontology and the reasoning and filtering of data. The three main semantic web standards that currently exist are Resource Description Framework (RDF), SPARQL (SPARQL Protocol and RDF Query Language), and OWL (Web Ontology Language). The goal of these standards is to present end-users with the information that they want at a particular time. The World Wide Web Consortium (W3C), which is the international standards organization leading the semantic web effort, has carried out many case studies that show how organizations are currently using semantic technologies in a variety of areas [24]. The W3C envisions the semantic web as an extension rather than a replacement of the current web. As discussed above, one of the main streams of effort in the semantic web concerns ontology, and includes ontology engineering, evolution, matching, mapping, and merging. The other main stream, which involves filtering and reasoning, includes the areas of content filtering, collaborative filtering, hybrid filtering, and reasoning.

 

3. Existing Challenges in Semantic Approaches to Big Data

The challenges involved in dealing with big data include access [9], capture, storage [3], search, sharing, transfer, analysis [4], and visualization, with respect to volume, velocity, variety, and value. However, the focus of our research efforts is on the following three areas:

Fig. 3.A depiction of linked data (Source: [11])

 

4. Existing Approaches to the Semantic Computing of Big Data

A number of approaches have been proposed for semantically dealing with the issues of big data. These proposed approaches work at different levels, and include areas such as ontology engineering, ontology evolution, reasoning, matching and representing big data. Some details of these approaches are as follows:

The following tables (Table 1, Table 2, Table 3, and Table 4) present summaries of semantic-based approaches that attempt to deal with big data. Table 1 presents our findings on semantics filtering and reasoning approaches proposed to deal with big data.

Table 1.Semantics filtering and reasoning approaches for big data

Table 2.Ontology evolution approaches for big data

Table 3.Ontology-based approaches for representing big data

Table 4.Ontology engineering approaches for big data

Table 2, below, presents our findings on ontology evolution approaches that have proposed for dealing with big data. It shows that only a few approaches have been proposed for ontology evolution in big data.

Table 3, below, presents our findings on ontology-based approaches for representing big data. There are only two proposed approaches in this area.

Table 4 presents our findings on ontology engineering approaches to dealing with big data. It shows that there are numerous proposed approaches in this area.

 

5. Existing Semantics-Based Tools for Big Data

Table 5 presents a summary of semantics-based tools for dealing with big data. Our research only discovered a limited number of such tools. A number of non-commercial tools are still ongoing projects, and many focus on specific domains. For example, SINA [29] focuses on the medical domain.

Table 5.Semantic/ontology-based tools for big data

 

6. Emerging Directions and Future Challenges for Semantics-Based Computing on Big Data

 

7. Conclusion

In this paper, we have attempted to answer questions on areas such as growth trends in data, how semantic computing can help to process huge amounts of data and uncover valuable information within it, what semantic-based approaches and tools are available for processing big data, and what the future looks like, in terms of the level of efficiency required to semantically process big data. We have determined that a semantics-based strategy is a suitable approach to dealing with big data issues. It is worth mentioning that this study examined the latest literature available, from 2011 to 2014. A total of 61 research papers were studied to explore the state of the art in this area. However, this review found that new solutions are needed to extract value, given the volume, variety, and velocity of big data. The use of semantic computing approaches to big data could enable end-users to consume information that is relevant to them. This study also offers recommendations that may encourage researchers to more deeply explore the ways in which semantic technology can improve the processing of big data. Our research may pave the way for the development of better basic knowledge on the semantic and computational issues of big data, and can act as a foundation for further studies within the field.