The Size Of Multimedia Is Increasing And The Need For Its Fast Retrieval

Cloud computing has really shifted the whole landscape of how companies do their databases now, and with this has brought the concept of big data.

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

Big Data refers to the large amounts of data that businesses deal with on a day-to-day basis. Big Data can be defined in many ways, but this is one of the most general definitions. One of the primary uses businesses have for Big Data is to provide assistance for decision making. In order for Big Data to be effective, organizations must utilize the data to their advantage. The quality of information is what matters, not the quantity. Organizations may have virtually unlimited amounts of data to work with, but they must locate the helpful portions of data in order for it to be effective and serve its purpose. In addition, some data may not be helpful at the present moment in time, but will be needed for analysis in the future (“What Is Big Data?”,

Big Data is the extremely large datasets that their sizes are beyond the ability of capturing,

Let us start by defining Big Data. Big Data is the term for datasets that are too big to process in traditional ways such as databases and simple analytics software. The size of big data is a moving target as storage and processing capacities grow. Because these datasets are so big (100 petabytes if you are Facebook for example) when working with them, you face a few challenges: storage space, searching, analysis, and

Big Data is a newer term that has been introduced to the technology world. By definition, the term “Big Data” refers to large amounts of complex sets of data, their relationships and their analysis. (Electronic Privacy Info Center). It can also be defined as a “collection of data from traditional and

Big data is an extremely important topic for future developments, growth trends and similarities between certain things. From a Microsoft blog published in 2013 big data is “the process of applying serious computing power” (HowieT, 2013). Another article describes big data as data that “exceeds the processing capacity of conventional database systems” (Dumbill, 2012). Based on these definitions and many more alike, big data refers to or can be described as recorded information that exceeds capacity. As brief as this is, data can be recorded using many instruments and even through observation. This topic is interesting to research and develop as new technologies are more capable at storing and reading mass data. With technology advancements, a method that took half a day, more than ten years ago, would only take a couple of minutes using present technologies. As big data is getting more widely used more businesses and enterprises will be interested in the trends shown.

Big data refers to the growing data a company experiences but this data has to be managed and processed to convert it into useful information. For example, a company XYZ just launched an e-commerce website and its receiving a

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

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.

Presence of big data is a very common phenomenon now days, specially when talking about medium to large size corporation. Manyika et al., in their article (James Manyika, 2011) defined the term big data as “large pools of data that can be captured, communicated, aggregated, stored, and analyzed”. To clarify they suggested that big data refers to data, whose size makes it impossible to be processed by the typical software used for database management. Gartner (Gartner, 2012)defined big data in terms of its characteristics of high volume, high velocity and high variety. By volume, he referred to the size of the data, by velocity he referred to the speed at which the data is created and by variety he referred to the range of types of data.

Big data is a vast amount of information, both structured and unstructured, unstructured is information that is not simple to interpret by traditional databases and structured information is the opposite, which is not too difficult to interpret data made form text or image. Structured or unstructured, the main business of big data is categorized

Big Data is defined by three characteristics: volume, velocity, and variety. Volume is the size of the data stored, and is measured in terabytes, petabytes, or exabytes. Velocity is the rate at which data is generated. Variety refers to the types of data, such as structured, semi-structured, or non-structured [Mahmood13]. Structured data is data that typically resides in a

Big Data is the data that surpasses the processing limit of the customary database frameworks. The data is huge, moves with more speed and does not fit the structure of the conventional database frameworks.

Data is very critical for any organization, industry or business process. Data which was in gigabytes or terabytes