Relational Database Management Systems: Unstructured Data

Data objects can model relational data or advanced data types such as graphics, movies, and audio. Smalltalk, C++, Java, and others are objects used in object-oriented data. The object-relational is a combination of relational and object-oriented databases. Traditional and advanced data types can be used to construct database management systems. These systems can connect to a company’s website and update records as needed. Database Approach The main purpose of a database is data storage that can be stored and retrieved when needed. A popular common language called structured query language (SQL) is used to store and retrieve data in relational database. This language enables the systems to run a report or modify data or remove the data from the database. A database management system (DBMS) controls all aspects of a database, this is not limited to the creation, maintenance, and use of database. The DBMS ensures proper applications are able to access the database. An important purpose of a DBMS is to maintain the data definitions (data dictionary) for all the data elements in the database. It also enforces data integrity and security measures. Data Models Data models provide a contextual framework and graphical representation that aid in the definition of data elements. In a relational database, the data model lays the foundation for the database and identifies important entities,

Data can be classified into three distinct categories: structured, unstructured, and semi-structured. It is important to understand these classifications and the challenges they present.

Data is defined as useful raw material which is intended to be useful for both the originator and for the intended receiver. Data consists largely of facts and figures ideal for communicating the intended meaning. This data can be interpreted and can be categorised as follows;

The data structure represents the logical relationships between data elements. In addition the data also determines the organizational structure, access methods, and alternative processing assosiativitas level for information.

The data that is being stored in a database is known as Meta data. Meta data is also recognized as schema for the real world data. It expresses that what sort of data will be stored in the database, what will be size of a assured attribute of the real world data, how many and what qualities will be used to collect the data about the entity in the database.

Data are raw facts that have no meaning until they are processed and organized to identify patterns and relationships between the data elements.

Metadata present a more complete picture of the data in the database than the data itself.

Traditionally data used for accounting purposes is structured in nature. Big data on the other hand can be both structured and unstructured.

The concept of metadata is "data about data" which has simple in principle but very confusing in details. Metadata appears in all kind of file, it acts like a person behind the scene, records many information about the file such as Internet history files have the information about what Web page is accessed at a certain timeline. There are 4 common types of metadata: Internet metadata, Document metadata, File system metadata, Picture metadata.

If there is data that is in numerical form this is called quantitative data if not then it is if referred to as qualitative data if there is no numerical value or data. For example qualitative data can be anything other than words for example it could be a recorded video or photographs these types can be identified as qualitative data

The unstructured dataset was downloaded from data.gov.uk website. It is a dataset of all meta-data for every dataset that are provided by data.gov.uk service (data.gov.uk, 2010). It consists of two CSV files that is linked by a dataset name entity: dataset.csv and resources.csv (data.gov.uk, 2010).

Data which is Elementary description of things, events, activities, and transactions that are recorded, classified, and stored but are not organized to convey any specific meaning.(CH01)Slide#9

Already some success is achieved from big data in some fields like Sloan digital sky survey so it’s mean there is some potential in big data and benefits are also real but still some challenges like scalability, heterogeneity, integration, privacy, security etc. need to be addressed for realizing full potential of big data. One of the major challenge is transformation of unstructured data to structured form for accurate and timely processing. Challenges with big data starts with very first phase of big data analysis pipeline that is data acquisition phase. It’s a challenging task to determine what data to keep, what to discard and how to efficiently store the data. Other challenges are faced in data cleaning, integration and data analysis phase of big data analysis pipeline.

* Build software to address and process the combination of structured and unstructured data sets

Databases always keep data as it stores it making the data more efficient and all the different types of data are generally categorised as a specific data type.