A time efficient and accurate retrieval of range aggregate queries using fuzzy clustering means (FCM) approach

Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Statistical analysis: Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of static variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the static variable might leads to the less efficient partitioning of tables. Findings: This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of static variable. Improvements: This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy

An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets

On Achieving Diversity in the Presence of Outliers in Participatory Camera Sensor Networks

This paper addresses the problem of collection and delivery of a representative subset of pictures, in participatory camera networks, to maximize coverage when a significant portion of the pictures may be redundant or irrelevant. Consider, for example, a rescue mission where volunteers and survivors of a large-scale disaster scout a wide area to capture pictures of damage in distressed neighborhoods, using handheld cameras, and report them to a rescue station. In this participatory camera network, a significant amount of pictures may be redundant (i.e., similar pictures may be reported by many) or irrelevant (i.e., may not document an event of interest). Given this pool of pictures, we aim to build a protocol to store and deliver a smaller subset of pictures, among all those taken, that minimizes redundancy and eliminates irrelevant objects and outliers. While previous work addressed removal of redundancy alone, doing so in the presence of outliers is tricky, because outliers, by their very nature, are different from other objects, causing redundancy minimizing algorithms to favor their inclusion, which is at odds with the goal of finding a representative subset. To eliminate both outliers and redundancy at the same time, two seemingly opposite objectives must be met together. The contribution of this paper lies in a new prioritization technique (and its in-network implementation) that minimizes redundancy among delivered pictures, while also reducing outliers.unpublishedis peer reviewe

Determinants in the on-line distribution of digital content: an exploratory analysis

This article shows the phases – and discusses the results – of an empirical analysis addressing the legal business models that are adopted online to distribute digital content

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

Modelling innovation support systems for development

The present article offers a concise theoretical conceptualization on the contribution of innovation to regional development. These concepts are closely related to geographical proximity, knowledge diffusion and filters, and clustering. Institutional innovation profiles and regional patterns of innovation are two mutually linked, novel conceptual elements in this article. Next to a theoretical framing, the paper offers also a new methodology to analyse institutional innovation profiles. Our case study addresses three Portuguese regions and their institutions, included in a web-based inventory of innovation agencies which offered the foundation for an extensive data base. This data set was analyzed by means of a recently developed Principal Coordinates Analysis followed by a Logistic Biplot approach (leading to a Voronoi mapping) to design a systemic typology of innovation structures where each institution is individually represented. There appears to be a significant difference in the regional innovation patterns resulting from the diverse institutional innovation profiles concerned. These profiles appear to be region-specific. Our conclusion highlights the main advantages in the use of the method used for policy-makers and business companies

Modelling innovation support systems for regional development - analysis of cluster structures in innovation in Portugal

The present article offers a concise theoretical conceptualization and operational analysis of the contribution of innovation to regional development. The latter concepts are closely related to geographical proximity, knowledge diffusion and filters and clustering. Institutional innovation profiles and regional patterns of innovation are two mutually linked, novel conceptual elements in this article. Next to a theoretical framing, the article employs the regional innovation systems concept as a vehicle to analyse institutional innovation profiles. Our case study addresses three Portuguese regions and their institutions, included in a web-based inventory of innovation agencies which offered the foundation for an extensive database. This data-set was analysed by means of a recently developed principal coordinates analysis followed by a Logistic Biplot approach (leading to a Voronoi mapping) to design a systemic typology of innovation structures where each institution is individually represented. There appears to be a significant difference in the regional innovation patterns resulting from the diverse institutional innovation profiles concerned. These profiles appear to be region specific. Our conclusion highlights the main advantages in the use of the method used for policy-makers and business companies.info:eu-repo/semantics/publishedVersio

Analyze Large Multidimensional Datasets Using Algebraic Topology

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper also investigate the possibility of Hadoop integration and the challenges that come with the framework