Data Structures and Algorithm Applications in Social Media - Essay Example

Data Structures and Algorithm Applications in Social Media Data Structures and Algorithm Applications in Social Media Data structures are the registers and memories in a computer, whilst algorithms are the pieces of information stored in the structures (Wirth, 1984). Algorithms are very useful in selecting the most relevant information during a search. There are recommended algorithms that are able to guide selective searches. In social media, such algorithms are able to identify a certain element, like a fiend, and ignore others. The algorithms are able to identify a certain topic as “trending”, from millions of topics on a particular social media site. These can; hence, be defined as procedures that transform input data into specific or desired output. This treatise discusses how the choice of data structures and algorithms affect social media. The paper also focuses on the differences in security procedures when it comes to large data sets as found in social media sites as compared to simpler and Java-driven websites. Social media are a group of websites that aid in social interaction, and consist of voluminous databases. In understanding data structures, one should take into account that they mostly define the memory mechanisms. In a computer, data is conveyed in chunks from the Random Access Memory (RAM) to the hard disk. The running time of this transfer is determined by the blocks/chunks. Data structures enhance efficiency in computing. The more powerful a computer is enables it to handle more complex applications, which lead to more complex calculations (Bender & Kuszmaul, 2014). The choices applied in the selection of data structures and algorithms determine whether a particular calculation will be carried out in many days or in just a few seconds. In social media, the information is stored in large database; hence, computations that result in searching of different information need to be performed at a very fast rate. This calls for keen choice of data structures and algorithms. In enhancing efficiency, a given solution will be termed efficient once it solves the problems assigned to it within the confines of the resource constraints (Shaffer, 2014). In deciding the data structure to use in a social media site, some of the steps to consider include; an analysis of the problem for determination of the applicable resource constrains, determination of the operation to be supported against the resource constrains, and selection of the most appropriate data structure for the given problem. In selecting a data structure, it is always good to consider the cost of the solution, which is a factor of the resources used, thus selecting the simplest most appropriate data structure (Bender & Kuszmaul, 2014). Algorithms, being the series of steps followed in solving a particular problem, differ. They should be as easy as possible, but also efficient in resource utilization. In the context of this discussion, the simplicity of the algorithms is ignored, since that is a fundamental concern of the designer. The efficiency of the algorithm and the effects on social media sites is looked at instead. The efficiency of an algorithm determines the time that a person takes to search a particular element in a site. This is measured through an empirical comparison of the manner in which programs run and through asymptotic analysis. The resources to consider in this case are time, space, the ease of use and the efforts of the programmer (Bronson, 2013). In social media such as Facebook, the choice of the structure or the data model is largely dependent on the analytics to be run, as well as the probable queries. A model such as the edge-centric graph is composed of a very large set of links between various people. A Facebook database will allow free transversal in the graph, which are made possible by various join notations. A single page in Facebook has the capability of filtering many items from the social graph. The filtered items must be tailored to each user. As data is relevant is such a site when a user is viewing it, such content is filtered when viewing. The strategy, as used on Facebook, requires extreme demands for the read operations. The data filtered must always meet the conditions for extreme efficiency, high availability and scalability during query operations (Bronson, 2013). A social graph is composed of objects such as posts and comments on Facebook, and that are connected by associations. The associations are the directed edges of the graphs that model how the nodes relate. These associations, on a user’s end are the manner in which elements connects such as friends to certain friends. The Facebook social graph was first built in MySQL with data being fetched using PHP. The fetched results were cached in memcache. This was then developed into such a way that they could allow clients to read as well as write various objects and their respective associations in the graph. The manner of controlling data led to a series of problems, which can be used in this context to highlight the requirements of selecting data structures and algorithms for social media. Inefficiencies in the edge lists must be sorted using efficient algorithms. Such algorithms should ensure that queries are fetched well from the edge list without having to involve the entire list as was the case in MySQL. There has to be algorithms that are responsible for the coordination of concurrent increments in updates. The data structure involved in this scenario is the memcache. Control logic for objects and associations should be fixed in the cache to avoid problems in distributed logic control. In some cache architecture, control logic is determined by clients who do not communicate. In the case of social media using such architecture, failure modes are grossly increased. A good solution to this case is using fixed API, which allows for the control logic to reside inside the cache, thus taking care of such problems. In Facebook, there is an additional problem caused by using data structures in MySQL arising from the master-slave replication that is asynchronous. This leads to additional expense in ‘read’ after ‘write’ consistencies. When data models are restricted to objects and the respective associations, the replica of the cache can update during the ‘write’ time. Graph semantics are then used to read cache messages in periods of concurrent Facebook updates. When such a procedure is invoked, there is consistency in the ‘read’ after ‘write’ operations that do not really require communication between regions (Bronson, 2013). Communication in social media cannot happen without databases. Graph databases solve the traditional problem of using relational databases in that they can yield fast answers to data structures to the type belonging to social networks. Another method of ensuring that data structures are efficiently utilized is through the use of relational databases that are schema free. Such databases have a way of focusing on the required queries before the complex computation processes. The graphs that perform these computations are composed of nodes, their relationships and attributes. When a query is initiated, like finding the number of friends belonging to a certain person, a transversal object is created, which navigates through the nodes, following the associations until it finds an answer to the query. Depending on the database in use, efficiency lies on the ability of such a database. When a relational database is used, cases requiring repeated probing through the network will require different queries for each question. This constrains the time resource. A database algorithm like Neo4j will solve such an issue using only one query. The Neo4j algorithm turns the data structures in the queries to a database through addition of persistence, caching and transactions. The algorithm also stores the data structures in the disk such that they are recoverable after a power botch. In social media, an algorithm that will make the process of searching through a network simplified is very essential (Wayner, 2010). There are various other options that can be used in the implementation of data structures and algorithms, though their effects should be checked cautiously. A platform such as Digg uses Cassandra, which assumes non SQL interactions. The problem with such an algorithm is consistency as updates delay, and it is common to find old links in new feeds. The algorithm is, however, more write friendly (Wayner, 2010). The question of security is always relevant when considering any application. In data structures and algorithms, many security features exist, but a cryptographic ability is essential. When a security policy is to be used in a certain social media site, the data structure for the particular policy needs to allow evaluation of the security sustainability of queries like precise algorithms. The particular data structure should be flexible in such a manner that it supports all incoming algorithms. Such a data structure should also support various use cases such as encryption, verification, signing and renewal of signatures (Kunz, Okunick, & Pordesch, 2009). Security mechanismsat the large databases such as the social media sites, therefore, do not differ much from security requirements in other smaller applications. It is, however, necessary to have flexible solutions while implementing security for big websites. In data replication, it is important to achieve data authentication, which will ascertain that the data items that are replicated at a certain server are similar to data items in the original server. The authentication can be made possible by using digital signatures. In this case, all data items are digitally signed and all the signatures replicated alongside the data items. This method is efficient for small data issues, but can prove complicated and inefficient in social media due to the constant evolution of data. To answer this problem, authenticated data structures are applied. These data structures use a three-tier model consisting of a user, a source and a responder. Proof of data validity is obtained and given to a user by an untrusted responder who answers queries regarding a data structure in the best interests of a trusted source (Tamassia, 2003). This is an additional difference to the security mechanism required for complex algorithms applicable in social media. In lieu of the discussions above, it is evident that the choice of data structures and algorithms is determinant of the success of a social media site. Efficiency in the search criteria and sorting of information is very crucial when it comes to posting and retrieval of updates and any other social media stuff. It is wise to use data structures and algorithms that do not constrain the most essential time and space resources, and at the same time, respect the efforts of the coder by not being overly difficult. References Bender, M & Kuszmaul, B. 2014. Data structures and algorithms for big databases. New York: State University of New York. Bronson, N. et al., 2013. TAO: Facebook’s Distributed Data Store for the Social Graph. USENIX Annual Technical Conference, 13: 49-60. Kunz, T, Okunick, F. & Pordesch, U., 2009. Data Structure for the security of cryptographic algorithms. Nework working group. Retrieved from http://tools.ietf.org/pdf/rfc5698.pdf Shaffer, C. 2014. A practical introduction to data structures and algorithm analysis: Java edition. Retrieved from http://home.deib.polimi.it/morzenti/Info3/notes.pdf Tamassia, R. 2003, Authenticated data structures. Verlag: Springer. Wayner, P., 2010, InforWord review: Databases primed for social networks. InfoWorld. Retrieved from http://www.infoworld.com/article/2627779/database/infoworld-review--databases-primed-for-social-networks.html Wirth, N., 1984. Data Structures and Algorithms, Zurich: Instut fur Informatik. Read More