It can be considered that the mishandling of big data mining is a crucial business problem for the digital library like 7.5.1 Victoria. After that, volume and scalability is a major type of problem for this business organisation to handle a large array of datasets of the library in an efficient way. After that, security vulnerability can be considered as another type of issues for the organisation to secure the valuable data information. The report will cover a identifies possible data sources associated with the decision-making incident of the library. The report will also cover a large array of recommendations on the efficient usage of big data mining to store a huge raw digital data information. After that, an appropriate data visualization technique will be added in the report based on business intelligence management strategy.
It can be observed that miss handling of large big data of the library is a crucial issue for the library 7.5.1 Victoria. After that, the second problem can be mentioned the designing of digital data information of the library which deals with the scalability and volume. The decision-making problem of the library also identifies the activities of the management to ensure the security measurements of these large data sets of a library.
It can be demonstrated that lacking decision-making practices is a crucial issue for the library management of the 7.5.1 Victoria. It can be considered that evidence-based management will provide a right direction for the librarians to accomplish their job. It can be observed that collecting evidence from different sources is the fundamental stage for the librarians for the evidence-based management process (Zakir, Seymour & Berg, 2015). The process will consist the procedures like reviewing the several kinds of literature, which will remain the raw data analysis (Rajaraman, 2016). It can be stated that the raw digital data information will not provide a guide for with the librarian to handle the management skills of enabling the big data. However, the digital analysis of the raw data will assist to take accurate decision for digital data handling of the library (Rahm, 2016).
For this reason, the library managers and the librarians who are associated with the decision-making process of the organisation should identify the appropriate structure of the institution and the experience of the managers in the library will assist to analysis the correct usage of experience. In-depth research knowledge has to be gathered by the library, managers and the libations to take decision-making approach of the library (Najafabadi Villanustre, Khoshgoftaar,., Seliya, Wald, & Muharemagic. 2015).
The data quality issues of the data preparation identify the several causes of the digital data vulnerability. It includes the heterogeneity of data information, which includes the data manipulation incidents in the software analysis for taking business decisions. It includes the issues associated with it such as personal data protection and the financial liabilities of the library organisation which generates the data breach issue. It also includes the security issues of the filtration of the endpoints inputs and validation of the data sets (Chen & Zhang, 2014). According to (Diamantoulakis Kapinas, & Karagiannidis. 2015), traditional database management systems are able to omit any potential security risks. It is observed that these traditional software systems are able to eliminate these security threats at a specific level. However, the big data systems are unable to keep the systems secure. Henceforth, the data quality issues also generate the protecting transaction of the data information of the library, which contains many types of sensitive information and the transaction logs for the daily activities of the library. It can be considered that digital assets or the computational security will constitute a distributed framework for building the authentic endpoint devices for the digital library like 7.5.1 Victoria (Bello-Orgaz, Jung & Camacho, 2016).
It can be considered that, big data solutions for handling the large array of sources which includes some aspects of the real-time processing of the digital information of the library. It can be considered that a library work contains the several activities such as issuing the digital books to the readers, authentic access for the readers in the login server of the library and most importantly the handling of numeric raw date information of a digital library. The architecture of the datasets of the decision-making software also includes the machine learning and the predictive analysis of the databases (Gandomi & Haider, 2015).
The components of the big data architecture consist the effective tools such as data storage volumes, data sources volume, real-time message ingestion, batch processing, stream processing and the machine learning process in the data analytics software.
The analysis software of the decision-making process also requires appropriate data modelling layer, which is the tabular data model or the multidimensional OLAP cube (Hashem Yaqoob, Anuar, Mokhtar, Gani, & Khan., 2015).
After that, the analysis of the big data solutions also consists of the repetitive type’s data processing operations, which can transfer the data between the multiple sinks and the sources. It works through loading the processing data information into the analytical store. It also pushes the analytical results into the company dashboard. For this reason, it will require the orchestrator technology, which is Apache Oozie and the Azure data factory (Assunção Calheiros,., Bianchi, S., Netto., & Buyya, 2015).
Lambda architecture will be needful for the data analysis purpose to take the business decision of the library management and to handle the corporate archives. The lambda architecture of the data analytics software will also include the batch layer path and the speed layer path in the system. The batch layer path of the system will also include the storage of the incoming data in the raw formation and it performs the batch processing of the data information. After that, it also includes the speed layer analyses in the real-time processing in the analysing software. The batch layer will also include the serving layer indexes for the efficient querying of the process. It can be observed that the digital infrastructure of the library 7.5.1 Victoria consists the several databases for the storage purpose which are library digital information and the information science sources, library and the information sciences abstracts, request or the multiple databases (Akter & Wamba, 2016). The archive storage of the library consists the several factors of raw digital information in the library. The raw digital archives consist of the multi packets information of a digital library. The corporate archive of the library contains the separate portal for the readers, separate portals for the management and the numeric huge amounts of eBooks of the library which stores in the cloud servers of the library (Tsai Lai, Chao, & Vasilakos,. 2015). The corporate archive folder of the library can be segmented as several ways such as array for scientific books, and for business journals. Array for the literature study and at last a separate archive for the daily newspapers and daily events of the library.
Figure 2: Corporate archive list of library
(Source: Hilbert, 2016)
It can be observed that a digital archive of the library will contain the primary sources of the digital information. After that, the second archive will contain the unique contents of every book available on the digital server of the library, which will attract the readers to efficiently use the library service. The library archives have to be encoded in the form of an encoded archival description XML format. The encoded archival description facility in the digital archives of the library will provide the standardised types electronic description for which it makes possible for proving the union access and resources to the repository distributorship.
After that, the digital preservation of the digital media and the information system is also a vital tool for building the corporate archives of the library. For this reason, the management of the library will require a virtual machine operating system, which will preserve the digital media information system (Hilbert, 2016).
Further, the library management will also require analytics tools like SAP and XERO to analysis the business data information of the library for taking the business decision in real time. The SAP analysis software will provide real-time processing dashboards to the readers (Suthaharan, 2014).
It is observed that an enterprise that is highly focused on data strategies have been able to generate a significant business value. Therefore, in order to retain the consumers in an effective manner, the academic library should focus on the customer analysis. In order to consider this step, the library should conduct research on the preferences of the consumers. This will help this organisation to develop a proper framework for handling big data significantly.
It can be said that an effective relationship between the librarian and book readers are necessary for the development of organisational productivity. According to (Khan Anjum, Soomro, & Tahir, 2015), Customer Relationship Management (CRM) applications are pivotal in evaluating the loyalty of consumers. Therefore, a proper CRM system in the Library would help to track the repeat purchases and amount spent by readers. This will allow this library to maintain a better flow of information, which is found absent in the current context.
It can be said the library might incorporate the collaborative filtering technique to isolate the problem of big data handling. Henceforth, the application of collaborative filters will help this organisation to focus specifically on a similar type of products, bought by the consumers. Therefore, developing a database by using collaborative filters will help this organisation to maintain the archive files of the library.
It can be recommended further that the library management design the books in the library according to the catalogued wise. For this reason, the metadata concepts can be used to analyse the text names of the files to handle the growing issues of the electronic publications.
After that, SAP software can be used for the data analysing purpose to take the management decision of the library. The facilities of the SAP software will provide the artificial intelligence technologies for analysis huge numbers of data sets in the library organisation. It can be considered that SAP will provide a real-time processing cloud environment for the analysis purpose of the business day of the library. It works on the ERP cloud environment which can offer the digital library a platform for the Enterprise resource planning. It assists a library organisation to press with separate database structure of the library. The library management authority has to install the applications ties of the SAP software which will provide the business logic structure and then the processing type’s client translations for the library organisation.
It can be recommended that, actuate business intelligence and reporting tools (BIRT) can be considered the effective tools to build and publish the reports for the data sources which ranges from typical business relational databases of the library corporate achieve.
Big data evaluation requires a thorough knowledge in this domain. On another hand, lack of proper infrastructure has been identified in the taken library. Therefore, the library should employ an IaaS and open software for handling big data of readers. In order to run this software properly, skilled individuals are required. Henceforth, recruiting and increasing the number of trained data scientists will help this library to solve this issue significantly.
Conclusion:
It can be concluded that the mishandling of the big data mining technologies can be considered as a major business problem for the 7.5.1 Victoria library organisations. It can be concluded that SAP environment can provide an effective data analytics solution to the library management organisation which will assist them in taking the business decisions in real time operating procedure. After that, Actuate business intelligence and the reporting tools can build the business intelligence manager for the library organisation. The artificial intelligence and the building logical network will assist the library management to provide automatic statistical analysis without any type of human interaction.
Reference:
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. Retrieved from https://link.springer.com/article/10.1007/s12525-016-0219-0
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15.
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Diamantoulakis, P. D., Kapinas, V. M., & Karagiannidis, G. K. (2015). Big data analytics for dynamic energy management in smart grids. Big Data Research, 2(3), 94-101.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. Retrieved from https://www.sciencedirect.com/science/article/pii/S0268401214001066
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
Hilbert, M. (2016). Big data for development: A review of promises and challenges. Development Policy Review, 34(1), 135-174.
Khan, Z., Anjum, A., Soomro, K., & Tahir, M. A. (2015). Towards cloud-based big data analytics for smart future cities. Journal of Cloud Computing, 4(1), 2. Retrieved from https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-015-0026-8
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1. Retrieved from https://link.springer.com/article/10.1186/s40537-014-0007-7
Rahm, E. (2016). Big Data Analytics. it-Information Technology, 58(4), 155-156.
Rajaraman, V. (2016). Big data analytics. Resonance, 21(8), 695-716.
Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70-73.
Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big Data, 2(1), 21. Retrieved from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0030-3
Zakir, J., Seymour, T., & Berg, K. (2015). BIG DATA ANALYTICS. Issues in Information Systems, 16(2).
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