Semantic Computing for Big Data: Approaches, Tools, and Emerging Directions (2011-2014)
Semantic Computing for Big Data: Approaches, Tools, and Emerging Directions (2011-2014)
KSII Transactions on Internet and Information Systems (TIIS). 2014. Jun, 8(6): 2022-2042
Copyright © 2014, Korean Society For Internet Information
  • Received : April 09, 2014
  • Accepted : June 09, 2014
  • Published : June 27, 2014
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About the Authors
Seung Ryul, Jeong
Graduate School of Business IT, Kookmin University, Seoul, South Korea
Imran, Ghani
Universiti Teknologi Malaysia (UTM), Skudai, Johor Bahru, Johor, Malaysia

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
T he 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:
  • According to[2]: Big data applies to information that cannot be processed or analyzed using traditional processes or tools.
  • According to[5]: Big data is a high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
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 ):
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Four key characteristics of big data
  • Volume. Machine-generated data is produced in much larger quantities than non-traditional data. For instance, a single jet engine can generate 10TB of data in 30 minutes. With more than 25,000 airline flights per day, the daily volume from just this single data source runs into the petabytes. Smart meters and heavy industrial equipment like that used in oil refineries and drilling rigs generate similar data volumes, compounding the problem of scalability.
  • Velocity. Social media data streams, while not as massive as machine-generated data, produce a large influx of opinions and relationships that are valuable to customer relationship management. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures that large volumes are continuously created (over 8TB per day).
  • Variety. Traditional data formats tend to be relatively well-defined by data schema, and change slowly. In contrast, non-traditional data formats are characterized by a dizzying rate of change. This is a common observation, that new services are added, new sensors are deployed, and new marketing campaigns are executed. The variety of data would need new data types to capture the variety of information and address issues of scalability.
  • Value. We classify value as involving both valuable information and economic value. Typically, there is good information hidden in larger bodies of non-traditional data, so the challenge is to identify what is valuable, and then transform and extract the relevant data for analysis. The economic values of different data vary significantly.
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.
  • Data growth worldwide:The predicted universal data growth between 2005 and 2020 is shown inFig. 2 (a), which indicates that by 2020, worldwide data growth will occur at a rate of 40,026 exabytes per year[56].
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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.
  • Data growth in China:The digital universe in China is expected to grow from 364 exabytes to 8.6 zettabytes between 2012 and 2020 (Fig. 2b). China’s share of the global digital universe will grow from 13% to 21% between 2012 and 2020. This means that, if it were printed out as text, China’s digital universe in 2020 would make up a stack of books reaching from Earth to Pluto and back 30 times[17].
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Data growth in China
  • Data growth in India:The digital universe in India is expected to grow from 127 exabytes to 2.9 zettabytes between 2012 and 2020 (Fig. 2c). India and China’s total shares of the global digital universe will grow by up to 29% by 2020[18].
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Data growth in India
  • Data growth in United States:The digital universe in the US is expected to grow from 898 exabytes to 6.6 zettabytes between 2012 and 2020 (Fig. 2d)[19].
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Data growth in US
  • Data growth in Western Europe:The digital universe in Western Europe is expected to grow from 538 exabytes to 5.0 zettabytes between 2012 and 2020 (Fig. 2e). This data, if printed, would make up a stack of books as high as 6.5 trillion Eifel Towers[20].
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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:
  • Large-scale taxonomy management for capturing big data:One highly pertinent and massive challenge is the hierarchical auto-arrangement of unstructured data into classified concepts. This should involve understanding data and related concepts and correctly assigning the data to the right concepts and categories without involving human/manual (domain/IT expert) interpretation[21]. This challenge is related to the volume and variety of big data that is generated by the web, social media, streaming, blogs, and other sources. The auto-arrangement of data according to concepts requires an understanding of contexts and contents (tag annotation, classes of data, etc.). This should be based on a comparison of new data with an existing knowledge base, as well as the development of taxonomies. In the case of big data, there could be billions of triples linked to each other. The processing of such a magnitude of data must involve efficient ontological engineering approaches. Annotation of the unstructured data of millions of web users based on ontology is a massive challenge, particularly in areas such as annotating browsing logs of user history over the last five years.
  • Uncovering and accessing relevant data from big data:Uncovering and accessing the relevant data within the large volume and variety of big data is an increasingly difficult task[9][21]. According to[13], issues arise due to the three dimensions of big data: volume, since massive amounts of data have been accumulated over the decades, velocity, since the amounts may be rapidly increasing, and variety, since the data are spread over different formats. While this is a typical problem of dealing with big data, applications can be used to auto-understand the dynamics of context. For instance, traffic data can be added to road maps to provide context on road conditions, probability of delay, length of projected obstructions, road conditions, etc. Big data and analytics tools are already available in the market, from companies such as IBM, SAS, SAP, and Oracle.
  • Linking big data:The concept of linked data (Fig. 3) has gained widespread currency, and has been successfully adopted by real-life IT-based companies, such as Google and Facebook. The concept of the Linked Open Data Cloud (LOD) has been used to integrate distributed data in the cloud.
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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:
  • Ontology engineering and big data
  • ○ Construction ontology: Large-scale structured, unstructured, and semi-structured data is transferred into ontological forms. A number of approaches have been proposed to deal with this issue[36][37][38][40][41][52][53].
  • ○ Big data access: Since accessing relevant information is an increasingly difficult task, the concept of ontology-based data access (OBDA)[9]has been proposed as a suitable approach. OBDA systems address the data access problem by presenting a general ontology-based and end-user oriented query interface over heterogeneous data sources. The core elements in a classical OBDA systems are an ontology describing the application domain and a set of mappings, relating the ontological terms with the schemata of the underlying data source.
  • ○ Understanding and linking big data: It is essential that the concept of linked data be used to understand relationships in a domain and link their concepts in taxonomies, through graphs or ontologies. Some examples of this approach can be seen in Google Knowledge Graph[10]and Facebook OpenGraph protocol[33], which use linked data to process information[50][51].
  • Ontology evolution for big data
  • ○ The ongoing stream of data involved in big data make it natural for ontological techniques to evolve. There are several approaches that deal with this issue[34][36][50][52].
  • Searching and filtering big data for relevant information
  • ○ Extracting and reasoning of big data
  • ▪ One of the primary areas of focus in dealing with big data is the extraction and reasoning of large-scale data. A number of approaches and tools are available for extracting relevant information from big data[34][35][39][44][45][48][58].
  • ○ Distributed (web-scale) reasoning on big data
  • ▪ Some emerging topics within the distributed reasoning approach on big data have been discussed in past studies[57][59][60][61].
  • ○ Representing big data
  • ▪ Representation of large-scale data poses significant challenges, as has been mentioned by past scholars[42][43].
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.
Semantics filtering and reasoning approaches for big data
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Semantics filtering and reasoning approaches for big data
Ontology evolution approaches for big data
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Ontology evolution approaches for big data
Ontology-based approaches for representing big data
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Ontology-based approaches for representing big data
Ontology engineering approaches for big data
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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.
Semantic/ontology-based tools for big data
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Semantic/ontology-based tools for big data
6. Emerging Directions and Future Challenges for Semantics-Based Computing on Big Data
  • In our opinion, there is a great deal of research potential in semantics-based approaches, as challenges still need to be addressed in the following areas:
  • Big Dirty Data: Ambiguous, Inconsistent, Inaccurate, Incomplete and Redundant
  • In almost all domains of IT systems, an enormous amount of scalable data is ambiguous, inconsistent, inaccurate, and incomplete. This is known as “dirty data.” In the context of big data, dirty data is also “big,” because big data is scalable and heterogeneous by nature. This data can thus be referred to as “big dirty data” (BDD). BDD could be one of the serious challenges facing semantic computing in a variety of areas (e.g., mapping, matching, aligning, reasoning, inferences). Although recent approaches have suggested solutions for managing large ontologies that contain redundant information[14], these approaches require human involvement to guide the computing process. For example, humans may need to manually set up the parameters for executing each step, starting with computing the alignment process between two concepts. Further steps must also be executed by humans, but may not be possible in dealing with BDD; auto-execution approaches are thus needed. With this in mind, new ontology capturing approaches should not only consider working on clean data, but also handling BDD.
  • Integrated Searching in Big Data
  • Apple has announced its intentions to enter the search engine arena. It plans to integrate search systems and involve Twitter search integration for personalized results[23], Wikipedia integration, and Bing Web search results. A typical search on Bing could then be “Show the Wikipedia profile of the hero from the most discussed movie on Twitter last week.” This would involve a real linked data platform. For such a vast integrated search engine, semantics-based optimization techniques will be needed to traverse, reason, and make inferences for the exploratory and iterative analysis of data. A tool such as Ontology 4[22]seems like a good starting point for this kind of 360-degree semantic search.
  • Transferring IT Solutions: From Reactive to Proactive
  • In our opinion, thus far, the main focus of IT approaches has been the processing of data stored through past events, which is referred to as the reactive semantic approach. However, by using big data, it is possible to analyze the existing large volume of data and use it for effective and efficient prediction of future events, which is called the proactive semantic approach. Such predictions should not be misinterpreted by analyzing sample data, and instead should be made by using pieces of ontological big data in context.
  • High-Speed (Streaming) Data Capturing and Consumption
  • The understanding, linking, and use of big data across domains is critical for future expansion. For instance, IBM’s utilities project deals with computing intelligence, power-usage monitoring, sensors, and grid computing. In addition, there is a need to capture concepts from high-speed streaming data, which is likely to test the ontology engineering community. For instance, Siemens constantly takes in several terabytes of temporal data from sensors, with an increase rate of about 30 gigabytes per day[12]. As mentioned by[16], if data is organized and integrated so that the context and the reason for collecting the data is clear, it is much easier to manage the information and gain value from the knowledge generated.
  • Security Issues in Big Data
  • The study in[15]has found that users have very poor awareness of how their data is being shared and used. They explain that as big linked data becomes more common, gaining users’ trust and consent will become more important, as taking advantage of personal information will become an important concern.
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.
Seung Ryul Jeong is a Professor in the Graduate School of Business IT at Kookmin University, Korea. He holds a B.A. in Economics from Sogang University, Korea, an M.S. in MIS from University of Wisconsin, and a Ph.D. in MIS from the University of South Carolina, U.S.A. Dr. Jeong has published extensively in the information systems field, with over 60 publications in refereed journals like Journal of MIS, Communications of the ACM, Information and Management, Journal of Systems and Software, among others. Dr. Jeong’s areas of interest are Process Management, Software Engineering, Systems Implementation, and Information Resource Management.
Imran Ghani is a Senior Lecturer at Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Campus. He received his Master of Information Technology Degree from UAAR (Pakistan), M.Sc. Computer Science from UTM (Malaysia) and Ph.D. from Kookmin University (South Korea). His research focus includes agile software development methods and practices, semantics techniques, secure software development life cycle, web services, software testing, enterprise architecture and software architecture.
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