Relationship between Big Data and the Semantic Web - Case Study Example

BIG DATA VS SEMANTIC WEB Big Data and Semantic Web Big Data Management Big data has become the face of leading companies since it has been noted to be creating new growth opportunities to the companies. Therefore, the main objective of big data management was to create a system of communication in the business that could help in sharing information to all the organizational stakeholders. Moreover, the information was intended to be relayed in a real time manner and with high frequency and efficiency (Summer School on Reasoning WEB & Koubarakis 2014, p.87). Big data management has created the ability of estimating business metrics including customer confidence. It should be noted that such estimation could not be achieved before the adoption of the Big Data Management. Additionally, it was also intended to increase transparency in the data being shared. In this case, any data that is not covered in the digital form will be captured in the Big Data (Heath & Bizer, 2011, p. 34). In other words, the big data management allows capturing of large amount of data in digital form. Finally, the big data management was intended to enable development of products and services of the new generation. The management and environment of the big data management surpasses the traditional data warehouse and relational databases platforms. This is because incorporates technologies that allows processing and storing data in nontransactional forms (Heath & Bizer, 2011, p. 49). The new technology of handling big data concentrates on the collection and analysis of big data that combines the use of the traditional data warehouse with logical data warehousing architectural systems. It is worth noting that the big data management must decide on what data to process. This determination is arrived at following a compliance reasons that determines what data to be stored and what data to be destroyed. Therefore, the technology requires a careful data classification that enables quicker and productive analysis of small data sets. A/B testing, integration, data fusion, machine learning, crowdsourcing, signal processing, natural language, simulation, genetic algorithm, visualization, and time series analysis are the incorporated technologies that are used in the management of the bid data. These technologies are considered exceptional in processing large data quantities efficiently and within tolerable period. These technologies have led to an increased demand of big data information management especially with organizations that generate enormous data on a daily basis (Heath & Bizer, 2011, p.162). For instance, governments often deal with large data. Therefore, they require big data managements. In this case, different governments have adopted the use of big data management technologies in handling their data. Additionally, big institutions have realized that the big data management is effective in terms of productivity, innovations, and cost. For governments, the data analysis is usually in different or multiple phases, that is, the local and central government data (Heath & Bizer, 2011, p. 52). Therefore, with big data management, the government will get the desired outcome. For instance, the Obama administration conducted big data management in the year 2012 with the aim of its adoption. The same was incorporated in the Obama administration with 84 different big data programs being spread six federal departments. Other nations or governments that have adopted the big data management include United Kingdom and India among others. Semantic Web The Semantic Web is an extended web that is based on World Wide Web Consortium (W3C) standards. The W3C standards allow exchange of protocol and common data format on the web. The most fundamental resource that sematic web rely on is the Resource Description Framework (RDF). For the W3C, it is the function of the semantic web to provide common framework for data sharing and reuse cross of the applications. Therefore, the aim of semantic web is find, share, and combine data or information easily. The semantic web not only works within a locality, but it was also designed to be a means of universal data exchange (Summer School on Reasoning WEB & Koubarakis 2014, p.123). Therefore, its metadata are intended to be diverse for reuse by the community as well as providing cost effect means of recording and sharing knowledge (Devedzic 2006, p.109). In other words, the semantic web was intended to embrace diversity since the web can present the information in natural language as well as using multimedia and graphic page layout (Heath & Bizer, 2011, p. 64). Finally, it is worth noting that the semantic web was intended to be friendly to users and help or facilitate sharing and processing of data that seems to be ambiguous, in unconstrained format, and difficult to be handled by the computer-processing unit. The functionality of the semantic web lies on a series of components that are supported by different technologies. There are a collection of structured and recovery links that link data that completes sematic web (Heath & Bizer, 2011, p. 81). These technologies are enabled by systems that provide formal data description of terms, concepts, and relationships. These descriptions are usually within a given knowledge domain. Some of specific technologies that support the functionality of semantic web include RDF, RDF Schemes (RDFS), RDF query language or the SPARQL, Simple Knowledge Organization System (SKOS), N- Triples, Notation3 (N3), Turtle, Rule Interchange Format (RIF), and Web Ontology Language (OWL). These technologies are interrelated; however, each has specific function to the operation of the web semantic. Web semantic technologies have been used in numerous areas in life. For instance, the same technologies can be used in the analysis of equities at workbenches (Heath & Bizer, 2011, p. 73). In this application, contents from different sites and news (Makeup Language) ML can be constituted up to 90+ aggregated feeds from international sources. For example, the feeds can be obtained from new agencies from different countries. These feeds are then classified continuously into smaller taxonomies, but with specific metadata that are generated automatically (Summer School on Reasoning WEB & Koubarakis 2014, p.144). This will be followed by creation of specific source argents that are generated semi-automatically. This kind of data generation and processing have proved vital in the equity market ontology that is constituted with over one million facts, that is relationship and entity instances. This application processes and presents data in a complex semantic query that involves ontology and metadata applications (Extended Semantic Web Conference & Cimiano 2013, p.143). Therefore, the analytical reports from these numerous data sources reflect the Intel corporation competition. Additionally, the same technology using the same application can be used in assessing threat to passengers for the national or homeland security (Extended Semantic Web Conference & Cimiano 2013, p. 102). In the application, the technology can gather security information from the licensed public and propriety knowledge sources. Each knowledge-based source will have over million instances (Heath & Bizer 2011, p.162). These will be analyzed and encoded in continuous and periodical computation structures with scalable metadata reports. Relationship between Big Data and the Semantic Web The term big data is used in describing the vast growth in data and its availability. This state of data change has been experienced in the last 20 years. Since the invention of the Internet, there has been a massive generation of data. The generated data must be made available for the users or intended users (Heath & Bizer, 2011, p.119). Data that are more massive started cropping with the invention of social Websites. For instance, by the year 2012, it was estimated that nearly 90 percent of world’s historical data had been generated within only 2 years, that is, between 2010 and 2012. These massive data resulted from social webs, which include Facebook, Twitter, and Linked among other social web (Heath & Bizer 2011, p.162). Therefore, the availability of semantic web and big data are generated. Therefore, the semantic web makes big data available to the developers. Additionally, since social and web data led to big data analysis, it worth noting that web semantic or social and web are vital in helping power new ways of detecting criminals, crediting score to customers, among other important activities. Web based information, some numeric, and some totally structured data have been noted to affect the development of the big data analytical (Devedzic 2006, p. 85). To correct the challenge, the developers need the semantic web that will facilitate or enable collaborative movement powered by the W3C proponents. In other words, the sematic web provides the consumers with effective and sufficient as well as consistent and easy ways to interpret the contents of big data thereby allowing effective and efficient big data analytical developments. Additionally, semantic web will allow big data analytics companies to make vital business decisions (International Conference on informatics and Semiotics in organizations (Liu 2014, p. 125). The semantic web also allows real time decision-making. For instance, Google may power companies that are running B2C form of businesses with semantic web to provide for their customers with experiences and better answers for their concerns instantly. With Google powering the semantic web technology, it is worth noting that the technology is built on gigantic database entities of people, things, and places (Extended Semantic Web Conference & Cimiano 2013, p. 192). These databases are highly structured so that they can be able to map relationships between commonalities and entities. The entities are also filled with knowledge and facts describing the entities (Heath & Bizer 2011, p.177). Amassing entities is vital since the entity knowledge will only relate to search of people, things, or places but not words. Therefore, the sematic web developers, the Google, must have enough knowledge about all entities in the world. In this case, the development of semantic web in itself usually leads to generation of big data that require metadata technology. The data should be enough to lead to accurate search answers. The successful use of the semantic web depends on the availability of the big data. Regardless of the intended structure, each user of the semantic web must always get the accurate answers to their search (Heath & Bizer, 2011, p. 98). This means that significant data must be amassed on specific user, habit, and history. Semantic web aims at enabling human beings and internet devices to communicate with each other. These entities will either share or reused data of different applications and of different organization. Despite the data source, all the intended information sharing between the internet and the user will be in real time (Extended Semantic Web Conference & Cimiano 2013, p. 161). These data to be shared in real time is the bi data. Therefore, the development and execution of the semantic web largely depend on the big data. The big data is usually categorized as 3V’s that is defined by volume, velocity, and variety. However, it is worth noting that this classification did not incorporate other vital big data aspects including value, veracity, variability, and visualization (Heath & Bizer, 2011, p.256). Veracity defines the precision or the correctness of the data while value relates to the economic benefit of the data to the society, organization and or companies. On the other hand, visualization is the property that makes the data easy to understand and read and variability is the characteristic that defines the changing nature of the data over time. Notably, it seems that these properties are shared by big data and semantic web (Borgman 2015, p.111). All the properties of big data including the 3V among other 4Vs must be manifested in the semantic web to enable such data relayed to the internet user with confident, efficiency, and with a lot of ease. However, the 7V’s that define the big data seems to be posing challenge to the semantic web (Extended Semantic Web Conference & Cimiano 2013, p. 173). This seems to be making it difficult for the semantic web to link and avail the intended data especially on the web created in large volumes (International Conference on informatics and Semiotics in organizations & Liu 2014, p.25). These problems are only encountered when semantic webs are thought of standing or operating as a different entity from the big data. In other words, big data forms the genesis of a perfect semantic web (Heath & Bizer, 2011, p.162). It should be noted that nearly all the technologies that are being development to enable effective functionality of the big data are perfect for semantic web. Some of these technologies, which include open source tools and Hadoop, have enabled the development of semantic web (Extended Semantic Web Conference & Cimiano 2013, p. 185). These technologies have made the linking and analysis of all data aspects better and cheaper. In the year 1998, Tim Berners-Lee described semantic web as machines connecting and communicating with other webs. The contemporary world of technology calls Lee machine or semantic web as the internet. The internet is also known as Big Data Setups that requires semantic fundamentals to operate (Borgman 2015, p.128). Connecting 25 or 50 billion devices of different, but those that interoperate, can only occur if the said devices are capable to browse, communicate and connect with web in the same way as humans do (Extended Semantic Web Conference & Cimiano 2013, p.243). Therefore, it is worth noting that any technology that involves big data will not only stimulate the semantic web development, but also requires a perfectly functioning semantic web. The same semantic web must take full advantage of the big data for it to function correctly (Devedzic 2006, p.85). Regardless of trying to determine what technology between big data and semantic web emerged first or depend on another, it is worth stating that the technologies supporting the big data are well developed and are helpful in developing semantic web. Summary of the Key Points The article “semantic web technologies for big data in sciences” is a contribution of Haongyan Wu and Yamaguchi. According to the duo, the scientific world is entering a big data era that is a breakthrough in science and technology. Other than the breakthrough in the size of data to be managed, the speed of data processing and storage has also increased. Additionally, the article addresses science of informatics that deals with the increasing volume of data as well as its complexity. The paper also addresses the key aspects and technologies behind semantic web and the big data. The graphs below show that data have immensely increased since 1985 a fact that is attributed to the introduction and use of internet especially following the introduction of social media and multimedia. With the increased data, the developers have seen the need to develop big data management as well as increasing the efficiency of data sharing among the data users. Different projects and activities across the world are associated with big data management techniques. Additionally, the use of big data management has found its application into vital security, environmental aspects, and social life of people. The same technology has been used in hospitals, security surveillance and natural disaster management as well as in social life. Despite the adoption and application of these new data management processing and sharing, the technology is facing challenges that include formal syntax, semantic and semantics among others. Finally, it is worth noting that the 7’Vs that describe effective big data seem to be posing challenge to the semantic web technology. Nonetheless, when semantic web is considered as part of big data management, then such challenges seems to be varnishing. Reference List Borgman, C. L., 2015, Big data, little data, no data: scholarship in the networked world. Devedzic, V., 2006, Semantic Web and education. New York, Springer. Extended Semantic Web Conference, & Cimiano, P., 2013, The semantic web semantics and big data : 10th International Conference, ESWC 2012, Montpellier, France, May 26-30, 2013 : proceedings. Heidelberg, Springer. http://dx.doi.org/10.1007/978-3-642-38288-8. Heath, T., & Bizer, C., 2011, Linked data evolving the web into a global data space. [S.l.], Morgan & Claypool. International Conference On Informatics And Semiotics In Organisations, & Liu, K., 2014, Service science and knowledge innovation: 15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations, ICISO 2014, Shanghai, China, May 23-24, 2014, Proceedings. International Semantic Web Conference, & Mika, P., 2014, The semantic web-- ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings. Part I Part I. http://dx.doi.org/10.1007/978-3-319-11964-9. Sikos, L. F., 2011, Web standards mastering HTML5, CSS3, and XML. New York, Apress. Summer School On Reasoning Web, & Koubarakis, M., 2014, Reasoning web: reasoning on the web in the big data era : 10th International Summer School 2014, Athens, Greece, September 8-13, 2014. Proceedings. Read More