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Records in the Web Lab database While the underlying collections are large, the sub-collections used for research are much smaller, rarely more than a few terabytes. The standard methodology is to use the database to identify a sub-collection defined by a set of pages or a set of links, and download it to another computer for analysis.
For most analyses, researchers do not need supercomputers. Look beyond the academic community The academic community has limited capacity to develop and maintain the complex software used for research on large collections.
Therefore we must be cautious about where we place our efforts and flexible in adopting software from other sources. Until recently, relational databases were the standard technology for large datasets, and they still have many virtues.
The data model is simple and well understood; there is a standard query language SQL with associated APIs; and mature products are available, both commercial and open source.
But relational databases rely on schemas, which are inherently inflexible, and need skilled administration when the datasets are large. Large databases require expensive hardware. For data-intensive research, the most important recent development is open source software for clusters of low cost computers.
This development is a response to the needs of the Internet industry, which employs programmers of varying expertise and experience to build very large applications on clusters of commodity computers. The current state of the art is to use special purpose file systems that manage the unreliable hardware, and the MapReduce paradigm to simplify programming [ 13 ][ 14 ].
While Google has the resources to create system software for its own use, others, such as Yahoo, Amazon, and IBM, have combined to build an open source suite of software. The Internet Archive is an important contributor.
For the Web Lab, the main components are: Hadoop, which provides a distributed file system for large clusters of unreliable computers and supports MapReduce programming [ 15 ]. The Lucene family of search engine software, which includes Nutch for indexing web data and SOLR for fielded searching [ 16 ].
The Heritrix web crawler, which was developed by the Internet Archive for its own use [ 17 ].
Hadoop did not exist when we began work on the Web Lab, but encouragement from the Internet Archive led us to track its development.
In early we experimented with Nutch and Hadoop on a shared cluster and in established a dedicated cluster. It has 60 computers, with totals of cores, GB memory, and TB of disk. The full pages of the four complete crawls are being loaded onto the cluster, together with several large sets of link data extracted from the Web Lab database.
This cluster has been a great success. It provides a flexible computing environment for data analysis that does not need exceptional computing skills. Several projects other than the Web Lab use the cluster, and it was recently used for a class on Information Retrieval taken by seventy students.
Expect researchers to understand computing, but do not require them to be experts People are the critical resource in data-intensive computing. The average researcher is not a computer expert and should not need to be. Yet, at present, every research project needs skilled programmers, and their scarcity limits the rate of research.
Perhaps the greatest challenge in eScience and eSocial science is to find ways to carry out research without being an expert in high-performance computing.
Typical researchers are scientists or social scientists with good quantitative skills and reasonable knowledge of computing. They are often skilled users of packages, such as the SAS statistical package.
They may be comfortable writing simple programs, perhaps in a domain specific language such as MatLab, but they are rightly reluctant to get bogged down in the complexities of parallel computing, undependable hardware, and database administration.
In the Web Lab, we have used three methods for researchers to extract and analyze data: Therefore, it is natural to be cautious in deciding who is authenticated to use the computers.Looking for your lab manual or online Step 1: Create an account Step 2: Click on HAVE A CODE and enter the code from your teacher or bookstore.
Step 3: Add to cart and checkout! Learn microbiology lab 4 with free interactive flashcards. Choose from different sets of microbiology lab 4 flashcards on Quizlet. Looking for your lab manual or online Step 1: Create an account Step 2: Click on HAVE A CODE and enter the code from your teacher or bookstore.
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Escience Lab 2 Chemistry Of Life Answers UMUC Biology / Lab 2: The Chemistry of Life INSTRUCTIONS: To conduct your laboratory exercises, use the Laboratory Manual that is available in the “Content” section of the Leo classroom or at the eScience Labs Student Portal.
Laboratory exercises on your CD are not be updated.
Booklist * Prices listed are estimates and subject to change without notice R - Required - Required by the instructor. C - Choice - Students will choose 1 . What’s the relationship between the DSO and the quant minor?
The quant minor is a psychology department offering focused on the kinds of statistics utilized within psychology and related fields, whereas the data science option is focused on statistical learning from a machine learning perspective which includes topics like database .