Programming, Research Paper Example

Strings are always considered as a vital part of any scripting language. For this reason, Java programming language considers strings as a primitive data type. In Java, comparison of equality for strings are denoted by ‘==’ and for inequality ‘!=’ within the values of string class (Dale & Weems, 2007). Likewise, primary data types have a direct support from the language itself and the engine is accountable for implementing strings. 80×86 programs normally utilize word values for three factors i.e. 16 bit integers (signed), 16 but integers (unsigned) and pointers that are also called offsets (Algorithm tutorials, n.d ). Subsequently, word is a largest data type, it is considered as a foundation for computational queries. Likewise, 80386 and later models supports 32 bit computational queries, however, several programs are reluctant for 32 bit instructions, as they will limit them for processing on 80386 and later models (Algorithm tutorials, n.d).

Many colleges and universities are now offering Java instead of C/C++. The transition from C to Java has concluded many programming conflict resulting in misconceptions and errors. However, there are other languages that have encountered misconceptions. Students can consult text books for identifying errors, in spite, mistakes are still made when students write programs. Most common confusing mistakes were found in error types such as syntax errors, logic errors and semantic errors. (Nauman Chaudhry, Kevin Shaw, & Abdelguerfi, 2005) some of the challenges associated with data processing include excessive use of processing resources on intermittent basis that may become a challenge. Likewise, the probable nature of data streams also establishes issues for existing query operators for a database along with specific implementations. As a result, no output will be produced from a continuous data stream.

For designing and implementing Asynchronous Distributed Systems, where robust data streams are incorporated along with exhaustive CPU utilization, the processes of this information are pushed to the distributed tier. Subsequently, this facilitates the development team to review the nodes independently for addressing messaging and scaling the processes. Moreover, the design and implementation of an Asynchronous Distributed Systems connects and synchronize by trading messages (Bode, Ludwig, Wolfgang Karl, & Roland Wismüller, ) and demonstrates two methods i.e. a reduced strategy that is capable of processing massive jobs that may require exhaustive computing instructions and memory along with a strategy highlighting work methodology. However, it is small and can address tasks associated with latency sensitivity via an open source deployment.

Moreover, two factors must be answered for addressing implementation issues in data processing i.e. where the data must be left and that will be the location of processing transaction and queries. If we assume a relational data model, in the replication process, system manages multiple instances of data stored at several different locations for robust data retrieval and fault tolerance. However, if a fragment of a relation is called replicated only, it if is saved in more than one locations or sites. Likewise, full replication is achieved by making the data available in all sites or locations. However, challenges that are associated with a data distribution design include these following factors:

• Why there is a requirement of fragmenting?
• What is the procedure to fragment?
• What percentage needs to be fragment?
• How to test adequacy?
• How to perform allocation?
• What are the information requirements?

References

Algorithm tutorials Retrieved 4/5/2012, 2012, from http://community.topcoder.com/tc?module=Static&d1=tutorials&d2=dataStructures

Bode, A., Ludwig, T. E., Wolfgang Karl, & Roland Wismüller. Euro-par 2000 parallel processing: 6th international euro-par conference, munich, germany, august 29-september 1, 2000: Proceedings Berlin ; Springer, c2000.

Dale, N. B., & Weems, C. (2007). Programming and problem solving using java . Sudbury, MA: Jones and Bartlett Publishers.

Nauman Chaudhry, Kevin Shaw, & Abdelguerfi, M. (2005). Stream data management . New York: Springer.