Unstructured Data Analytics

Unstructured data, which is the heart of big data, are generating in every moment due to the revolution of Internet. Relational data model is not the right tool to handle such unstructured data because it has limitation of scalability, accessibility, flexibility, agility and in addition redundancy in database. In order to resolve those flaws of relational data model, a number of non-relational data models have been invented. Among those data models, this paper focuses on document oriented data model on some cosmetic products, explores the different shortcomings of relational data model compared to non-relational data model and suggests the best data model for cosmetic product.

the law is a vast and complicated body of knowledge, and being able to access the right information quickly andaccurately can make the difference. Having access to information is essential to providing the best possible legal advice and representation to clients.

Unstructured data, which is the heart of big data, are generating in every moment due to the revolution of Internet. Relational data model is not the right tool to handle such unstructured data because it has limitation of scalability, accessibility, flexibility, agility and in addition redundancy in database. In order to resolve those flaws of relational data model, a number of non-relational data models have been invented. Among those data models, this paper focuses on document oriented data model on some cosmetic products, explores the different shortcomings of relational data model compared to non-relational data model and suggests the best data model for cosmetic product.

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This paper examines the key lines of inquiry that have been used in research focused on the identity, dynamics, and diffusion of MIS, as well as the strengths and weaknesses associated with each approach. We present five primary means: (1) citation analysis, (2) classification analysis, including metaanalysis, (3) editorials and opinion pieces, (4) historical surveys of previous work, and (5) forums. We use the term "line of inquiry" since this allows us some latitude in considering dissimilar approaches^methods as well as communication channels. Using examples from the published literature on the status of MIS, we define and illustrate the five approaches. Subsequently, we discuss the strengths and weaknesses of each. Where possible, we extend the discussion to consider the implications of these lines of inquiry for future research.

We also owe a great debt of gratitude to editors of several of the journals covered by this analysis for going to great lengths to grant us access to data. Table 7. Systems and Software Engineering (SSE) research Community Topic Areas Community Journals Topics #Abs Definition Factor score Journal Factor loading Programs 229 Research on programs and programming languages. 7.38 IEEE Transactions on Software Engineering .894 Test 152 Research on testing of software. 6.38 ACM Transactions on Software Engineering and Methodology .789 Software 226 Research on software reliability and failure as well as processes to avoid low reliability.

Unstructured data, which is the heart of big data, are generating in every moment due to the revolution of Internet. Relational data model is not the right tool to handle such unstructured data because it has limitation of scalability, accessibility, flexibility, agility and in addition redundancy in database. In order to resolve those flaws of relational data model, a number of non-relational data models have been invented. Among those data models, this paper focuses on document oriented data model on some cosmetic products, explores the different shortcomings of relational data model compared to non-relational data model and suggests the best data model for cosmetic product.

Unstructured data, which is the heart of big data, are generating in every moment due to the revolution of Internet. Relational data model is not the right tool to handle such unstructured data because it has limitation of scalability, accessibility, flexibility, agility and in addition redundancy in database. In order to resolve those flaws of relational data model, a number of non-relational data models have been invented. Among those data models, this paper focuses on document oriented data model on some cosmetic products, explores the different shortcomings of relational data model compared to non-relational data model and suggests the best data model for cosmetic product.

Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data has become imperative for organizational strategic decision making. In this research, we develop a framework that captures and analyses unstructured data using machine learning techniques and integrates knowledge and insight gained from the data into traditional knowledge management systems. Unlike most frameworks published in the literature that focuses on a specific type of unstructured data, our frameworks cut across the varieties of unstructured data ranging from textual data from social network sites, online forums, discussion boards, reviews to audio data, image data and video data. We highlight some preprocessing and processing techniques for these data and also highlight some standard output. We evaluate the framework by developing a textual data application programming interface (API) using python and beautiful soup and we perform sentiment analysis on the students' review data collected through the API.

Big Data has gained an enormous momentum the past few years because of the tremendous volume of generated and processed Data from diverse application domains. Nowadays, it is estimated that 80% of all the generated data is unstructured. Evaluating the quality of Big data has been identified to be essential to guarantee data quality dimensions including for example completeness, and accuracy. Current initiatives for unstructured data quality evaluation are still under investigations. In this paper, we propose a quality evaluation model to handle quality of Unstructured Big Data (UBD). The later captures and discover first key properties of unstructured big data and its characteristics, provides some comprehensive mechanisms to sample, profile the UBD dataset and extract features and characteristics from heterogeneous data types in different formats. A Data Quality repository manage relationships between Data quality dimensions, quality Metrics, features extraction methods, mining methodologies, data types and data domains. An analysis of the samples provides a data profile of UBD. This profile is extended to a quality profile that contains the quality mapping with selected features for quality assessment. We developed an UBD quality assessment model that handles all the processes from the UBD profiling exploration to the Quality report. The model provides an initial blueprint for quality estimation of unstructured Big data. It also, states a set of quality characteristics and indicators that can be used to outline an initial data quality schema of UBD.

The Information Systems field is structured by the research topics emphasized by communities of journals. The Latent Categorization Method categorized and automatically named IS research topics in 14,510 abstracts from 65 Information Systems journals. These topics were clustered into seven intellectual communities based on publication patterns. The technique develops categories from the data itself, it is replicable, is relatively insensitive to the size of the text units, and it avoids many of the problems that frequently accompany human categorization. As such LCM provides a new approach to analyzing a wide array of textual data.