Shailaja Shirwaikar

An Exploratory Study of the Security Design Pattern Landscape and their Classification

Security is a critical part of information systems and must be integrated into every aspect of the system. It requires a lot of expertise to design and implement secure systems due to the broad coverage of security issues and threats. A good system design is based on sound software engineering principles which leverages proven best practices in the form of standard guidelines and design patterns. A design pattern represents a reusable solution to a recurring problem in a specific context. The current security design pattern landscape contains several patterns, pattern catalogs and pattern classification schemes. To apply appropriate patterns for a specific problem context, a deeper understanding of this domain is essential. A survey of patterns and their classification schemes will aid in understanding pattern coverage and identifying gaps. In this paper, the authors have presented a detailed exploratory study of the security design pattern landscape. Based on their study, the authors have identified shortcomings and presented future research directions.

Analytics on Talent Search Examination Data

Learning analytics and educational data mining has greatly supported the process of assessing and improving the quality of education. While learning analytics has a longer development cycle, educational data mining suffers from the inadequacy of data captured through learning processes. The data captured from examination process can be suitably extended to perform some descriptive and predictive analytics. This paper demonstrates the possibility of actionable analytics on the data collected from talent search examination process by adding to it some data pre-processing steps. The analytics provides some insight into the learners characteristics and demonstrates how analytics on examination data can be a major support for bringing the quality in education field.

Attribute Analysis using Fuzzy Association Rules and Interestingness Measures

While describing cross price elasticity of demand the terms complimentary and substitute goods are often used in Economics. If two goods are substitute if having one, fulfills consumers requirements thus demand for other decreases while in case of complimentary goods, using more of good one increases requirement for the other. This categorization of items has several applications as in planning replacements in case of scarcity, bundling or packaging of goods, cross-selling etc. On similar lines this relationship between two items can be extended to quantified attributes. This paper uses fuzzy association rule and fuzzy support to define complimentary and substitute attributes and illustrates them using known data sets and discusses their applicability.

Hierarchical Cluster Analysis On Security Design Patterns

Integrating security into enterprise level applications requires expertise and experience. Frameworks, tools and guidelines have been developed for the incorporation of security. Expert guidance is also encapsulated in the form of security design patterns which provide reusable solutions to recurring security problems. This field has been greatly enriched by contributions from various researchers since the emergence of the first security design patterns in 1997. However, the pattern landscape is very vast and unorganized in terms of the number of patterns and also their classification. This makes it difficult to apply design patterns in practice, leading to their poor adoption. Analytical tools can be applied on patterns if they are classified according to a common set of criteria. Hierarchical clustering is a widely used data analytical tool for creating groups of similar data items. Grouping of patterns will give additional insights into their relationships and hierarchy; thereby improving their usefulness and applicability. In this paper, we have applied hierarchical clustering to a set of security design patterns which are classified according to a chosen set of criteria.

An analytical study of security patterns

Security is a critical component in information systems today. Designing and implementing secure systems requires a lot of skill and expertise. A secure system is based on sound software engineering principles and proven best practices in the form of guidelines and design patterns. Since Yoder and Barcalows seminal work in 1997 on security patterns, several patterns and pattern catalogs have emerged. Today, the security pattern landscape is very vast and complex. Hence, proper organization and classification of patterns is important so that appropriate patterns can be used in a specific problem context. Patterns have been classified using various methodologies and criteria. There are many overlaps in the classification schemes and they do not cover all patterns. We propose that the classification of security patterns based on a common set of criteria and the use of analytical methods and tools will give additional insight into the relations, hierarchy and grouping of patterns, whereby their applicability can be improved. This is also important to understand pattern coverage and perform gap analysis. In this paper, we present an analytical study of security patterns and use hierarchical clustering to organize the patterns. The paper also presents an algorithm for pattern selection based on security goals.

Semantic Clustering Driven Approaches to Recommender Systems

Recommender Systems (RS) have increasingly evolved from novelties used by few E-commerce sites to an essential component of business tools handling the world of E-commerce. Recommender Systems have been widely used for product recommendations such as books and movies as well as, it is also gaining ground in service recommendations such as hotels, restaurants and travel attractions. Collaborative filtering based on reviews and ratings is usually applied that uses Clustering technique. The primary step of converting textual reviews into a Feature Matrix (FM) can be greatly refined by using semantic similarity between terms. In this paper Wordnet based Synset grouping approach is presented that not only reduces dimensions in FM but also generates Feature vectors (FV) for each cluster with significantly improved cluster quality. The paper presents a three step approach of validating the reviews, grouping of reviews and review based recommendations using Feature vector. Real datasets extracted from travel sites are used for experiments.

A Distributed, Scalable Computing Facility for Big Data Analytics in Atmospheric Physics

Technological advancements in computing and communication have led to a flood of data from different domains like healthcare, social networks, Internet commerce and finance. Over the past few years a larger chunk of data comes from the domain of scientific applications, using simulated experiments or collected using sensors. This development calls for new architectural models for data acquisition, storage, and large-scale data analytics.

A distributed, scalable parallelization of fuzzy c-means algorithm

Distributed Applications from different domains like Health care, E-Commerce, science, social networks etc., tend to generate large volumes of heterogeneous data that grow exponentially over a period of time leading to big data sets. Descriptive Analytics, on big data sets, pose a great challenge for traditional data analytical tools, since it is to be performed on the full data set, unlike predictive analytics which is done on training samples. Clustering is a commonly used descriptive analytics method, that requires necessary support for execution on big data sets. Clustering is a process of generating groups from a data set, such that members within a group have strong affinity to each other, and very weak affinity to members across the groups. The most common clustering algorithm is the K-means algorithm, which gives crisp clusters, where each data member belongs to exactly one cluster, This algorithm tends to provide less accuracy, when the nature of the data members is such that, they tend to show affinity towards more than one cluster. This vagueness in the data is better captured by the Fuzzy c-Means algorithm, which is seen as an improvement over the k-means, giving a set of fuzzy clusters, where the affinity to each of the clusters is defined by a membership value. In this paper, we present an implementation of fuzzy c-means algorithm, as a parallelized algorithm, for big data analytics, using the MapReduce programming model on Hadoop framework. A detailed performance analysis of the implementation, using various parameters of the algorithm is presented on a real data set from the machine library.

Conceptual model for a data warehouse on the web

All organizations big or small need to manage their data effectively, both the structured data stored in operational databases and the unstructured data that is spread out across documents. As the data size grows, it becomes essential to move the data to a warehouse so that data is available for adhoc queries and analysis. This data repository of both useful data and documents along with meta data need to be moved to the Web so that the data is easily accessible to all the stakeholders. This warehouse on the web can be constructed using Resource Oriented architecture that has the good properties of addressability, statelessness, scalability, connectedness and simple unified interface. Every physical or conceptual object that is part of an organization leaves an information footprint in form of unstructured or semi structured documents and data that are captured in a set of attributes. The relevant attributes of objects and their relationships with other objects exhibit a conceptual hierarchy that can be best modeled by resources on the web. This paper presents a Conceptual Model for constructing a Data Warehouse on the web server for storage, versioning, metadata, retrieval and adhoc queries. It will hold the documents generated, received, modified by the organization and the related data. A framework is designed with a simplified interface that can be used to construct, view and interrogate such a data warehouse.