Middleware Framework For Streaming Data Essay

The world has become a very complex place. From the earliest days of computers, storing and manipulating the data have been a major application focus. There has been enormous growth in the computer and database applications over the past two decades. Database is a group of data which consists of tables, schema, reports and views. Database may be of any size and complexity. For example, a salesperson may maintain a small database of customer contacts which consists of few megabytes in his or her computer. A large

There are a lot of system requirements and assumptions made in this paper. The query model is assumed to have simple read and write operations to data nodes that are identified uniquely by a key. This assumption is made based on the fact that most of the amazon applications does not require a relational schema and can work with simple queries.

The research proposal can be developed from the topic of big data to match up the demands of enormous data flow in the dynamic world. Data visualization tools at significant cost can help up in analytics of big data and can form an innovative research proposal for analysis and extensive research. The real time data and the use of techniques of big data in this domain can form an excellent topic of research calling for formulating methodologies and strategies to tap the untapped potential of this field and to experiment more in the field of research.

Clustering Databases provides us with an advantage of Cost effectiveness. “A grouping of related items stored together for efficiency of access and resource utilization.” In Relational Database the notion of clustering is straight forward. The physical clustering of data in this environment is extremely important to database performance since the architecture of RDBMS systems is heavily server-centric. In this we describe the Advantages done for clustering through replication.

Technology has changed vastly in the last fifty years. These changes can be seen in many areas such as airline reservation systems, automated teller machines, mobile phones, the internet, world wide web and sensor networks. These sources generate a lot of data; heterogeneous data means coming from different sources. Businesses have focused on automating their processes, and they have realized that data integration is becoming complicated. Thus, it is important to understand how to integrate and analyze data in real-time.

Today, interconnected networks around the globe stream data to a profusion of devices that range from powerful mainframe supercomputers, and massive grids of commodity machines to smartphones and tiny single-board computers embedded in home appliances. Most of the flow of data is done through the Internet or over other large TCP/IP networks that utilize HTTP communications. For that reason, most of this data is in a semi-structured state that is designed not for storage in a relational database, but for communication over HTTP. This data often arrives and leaves in a voluminous stream. It is therefore difficult for organizations to convert the data quickly to and from a format that is conforms to the rigid tabular structure of a relational schema.

Today ‘Big data’ is more popular than before, the performance of a database is becoming extremely important, including family life, school studying, office work and all business. Providing reliable and faster database services is the goal of organizations including any business, schools, and

By carrying out research with different datasets, it was clearly observed that hybrid and filestream data type approach is faster

This is a data management issue. Traditional database management systems are not capable of dealing with such continuous high data rate. Novel indexing, storage and querying techniques are required to handle this non stopping fluctuated flow of information streams.

This chapter examines about the real time analytics for different software applications and the current implementation to gather the need. It also constitutes the tabular evaluation between the current implementations.

In every moment of our lives, we handle a tremendous amount of large data, this large data will become a store of values if we could be turned it into searchable information involving analysis steps. The big challenge is that 90% of the large data are unstructured data which is growing faster than structured data, unstructured data as a data warehouse come without any predefined data structure and not appropriate with any relational database schema.

Another way to provide approximate answer for queries on data stream is to evaluate the queries only over sliding windows of recent stream data rather than the entire history of data stream. This method is more desirable by most of the real-world applications as recent data is more relevant than the old data. Sliding windows on data stream is well-defined and deterministic in nature and will not produce bad approximation.

Abstract: Cloud technology is very constructive and usefulin present new technological era, where a person uses the internet and the remote servers to give and maintain data as well as applications. Such applications in turn can be used by the end users via the cloud communications without any installation. Moreover, the end users‟ data files can be accessed and manipulated from any other computer using the internet services. Despite the flexibility of data and application accessing and usage that cloud computing environments provide, there are many questions still coming up on how to gain a trusted environment that protect data and applications in clouds from hackers and intruders. Cloud storage should be able to store and share data securely, efficiently, and flexibly with others in cloud storage. The costs and complexities involved generally increase with the number of the decryption keys to be shared. The encryption key and decryption key are different in public key encryption. Since we are proposing new era of Aggregate key cryptography. To produce constant length ciphertext is also one of important task that we have materialized. In this paper, we propose a simple, efficient, and publicly verifiable approach to ensure cloud data security while sharing between

The ever-widening realm of big data has created an expanding frontier of exploration for the creation of new methods of data analysis in order to produce actionable knowledge for the benefit of organizations everywhere. Companies amass enormous troves of data every day. Keeping this data housed in a fashion that maximizes storage efficiency and in a format optimized for query and analysis is paramount for effective data warehousing. Many database structures exist for the storage, arrangement, and accessing of data, but large databases and online analytical processing (OLAP) benefit from specific qualities. In these databases, compression and rapid querying are the main enabling qualities sought for analytical data stores and data warehouses. Columnar (or column-oriented) relational databases (RDBMS) offer these and other benefits, which is why it is a popular database scheme for analytical systems. Specifically, the vertical arrangement of records is optimal for selecting the sum, average, or a count of total record attributes because one horizontal read yields all values of an attribute. Otherwise, a physical disk must seek over and past unwanted attributes of the records to provide the same

These created indices are refined constantly as more and more queries are processed. The proposed holistic indexing takes the state of-the-art adaptive indexing concepts a big step further by introducing the idea of a system which refines the index space continuously and never stops while taking educated decisions about which index to be incrementally refined next based on continuous knowledge acquirement about the running workload and