Holistic Indexing On Main Memory Column Stores

1. Sources are cited in the body of the paper using APA format (up to 5 points)

With the advent of computer technology in 1990’s the need to search large databases was increasingly becoming vital. The search engines prior to PageRank had limitations, the then most widely used algorithm used text based indexes to provide search results on World Wide Web however had limitations of improper search results as the logic used by the search engines looked at the number of occurrences of the search word in webpage which sometimes resulted in improper search results. Another technique used during the time was based on variations of standard vector space model – i.e. search based on how recent the webpage was updated and/or how close the search terms are to the

Guidelines: It’s always best to introduce a paper to the reader. It sets the tone and provides an overview of what will be covered and what the goals are.

Kimura, H., Huo, G., Rasin, A., Madden, S., & Zdonik, S. B. (2010). CORADD: Correlation aware database designer for materialized views and indexes. Proceedings of the VLDB Endowment, 3(1-2), 1103-1113.

a. Your paper should be more than a mere compilation of quotations. Only quote material that supports your argument and make sure that you make clear why the quotation is relevant. Your explanation should do more than merely repeat what the quotation says.

Apply each of the following questions to the paper you’ve selected to read. Provide thorough and thoughtful answers so the author can easily and appropriately revise.

I found the technique of the permuterm indexing especially of note. While it seems like it results in a massive index it was interesting to think of how that could be applied along with other ideas to mitigate human imperfections for searches.

Are topic sentences and transitions used to deliver the paper in a coherent manner? What suggestions can you offer to increase organization and structure? The different points are clearly stated and transition well. Referring back to my last answer, the author needs to put his research into an essay.

1)Number each paragraph in the paper: 3
2) List the main point (s) of each paragraph

CONSTRAINTS (i) (ii) (iii) Since the DBMS being used is MS EXCESS 2000, which is not a very powerful DBMS, it will not be able to store a very large number of records. Due to limited features of DBMS being used, performance tuning features will not be applied to the queries and thus the system will become slow with the increase in number of records being used. Due to limited features of DBMS being used, database auditing will not be provided.

The volume and density of streaming data have also been rapidly growing. Appropriate indexing approaches are essential to handle fast incoming data and to process continuous flow of queries. A new indexed structure is proposed to reduce the space cost and speed up the retrieval from data storage. ACBSD (Adaptive Clustering Based Stream Data) is proposed to index and retrieve streaming data efficiently. ACBSD-tree is proposed which aims to address the three main challenges in data indexing (1) scalable insert, (2) fast search, and (3) scalable deletion. The tree-based indexing structure requires much less space than linear structure.

( Query Optimization in Database Systems) To sum it up query optimization is important so that these costs can be reduced as much as possible even as the amount of data increases.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

The modern RDBMS advancements are not capable of supporting unstructured information with ideal space necessity. The plan winds up plainly mind-boggling and is henceforth troublesome for designers. The requirement for unstructured information administration is so annoying with conventional RDBMS arrangements (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). Moreover, RDBMS turns out to be an exorbitant answer for creating light-footed web applications with direct information investigation necessities. NoSQL is developing as a proficient possibility in this situation, which connects the issues related with RDBMS innovation. The market development can credit to creative dispatches of NoSQL arrangements, and collective endeavors by NoSQL sellers and clients. The endeavors of organizations, to enhance their market offerings, are creating the request of NoSQL, as a back-end bolster (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). The emergence of agile software development is creating the demand for NoSQL (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). They offer users much more avenues to accept data in many different forms. NoSQL is adaptable as SQL but offers many more uses that can apply to many organizations.

D. Horie et al. (2008)[26] Modern day's queries are posed on database spread across the globe, this may impose a challenge on processing queries efficiently, and a strategy is required to generate optimal query plans. In distributed relational database systems, due to partitioning or replication on relations at multiple sites, the relations required by a query to answer, may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent alternatives or query plans for a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, the query plan with most cost-effective option for query processing is measured necessary and must be