Location Based Services (LBS) are expected to become one of the major drivers of ubiquitous services due to recent inception of GPS-enabled mobile devices, the development of Web2.0 paradigm, and emergence of 3G broadband networks. Having this vision in mind, Community Context-attribute-oriented Collaborative Information Environment (CCCIE) based Autonomous Decentralized Community System (ADCS) is proposed to enable provision of services to specific users in specific place at specific time considering various context-attributes. This paper presents autonomous community construction technology that share service discovered by one member among others in flexible way to improve timeliness and reduce network cost. In order to meet crucial goal of real-time and context-aware community construction (provision of service/ service information to users with common interests), and defining flexible service area in highly dynamic operating environment of ADCS, proposed progressive ripple based service discovery technique introduces novel idea of snail's pace and steady advancing search followed by swift boundary confining mechanism; while service area construction shares the discovered service among members in defined area to further improve timeliness and reduce network cost. Analysis and empirical results verify the effectiveness of the proposed technique.

If the signal is not Gaussian, then the power spectral density (PSD) approach is insufficient to analyze signals and we resort to estimate the higher order spectra of the signal. However, estimation of the higher order spectra is even more time consuming, for example, the complexity of trispectrum is O(N 4). This problem becomes even more serious when short time Fourier transform (STFT) is computed - computation of the trispectrum is required after every shift of the window. In this paper, a method to recursively compute trispectrum has been presented and it is shown that the computational complexity, for a window size of N, is reduced to be O(N 3) and is the same as the space complexity.

Power Spectral Density (PSD) computed by taking the Fourier transform of auto-correlation functions (Wiener-Khintchine Theorem) gives better result, in case of noisy data, as compared to the Periodogram approach in case the signal is Gaussian. However, the computational complexity of Wiener-Khintchine approach is more than that of the Periodogram approach. For the computation of short time Fourier transform (STFT), this problem becomes even more prominent where computation of PSD is required after every shift in the window under analysis. This paper presents a recursive form of PSD to reduce the complexity. If the signal is not Gaussian, the PSD approach is insufficient and we estimate the higher order spectra of the signal. Estimation of higher order spectra is even more time consuming. In this paper, recursive versions for computation of bispectrum has been presented as well. The computational complexity of PSD and bispectrum for a window size of N, are O(N) and O(N2) respectively.

Autonomous Decentralized Community System (ADCS) makes its basis on offering customized and dynamic services to group of end-users having common preferences at specified time and location. Owing to extreme dynamism in the system caused by rapidly varying user's demands, and severe mobility of users, it is quite difficult to assure timely service provision to all community members. This paper presents autonomous decentralized community system construction by autonomous division and integration technologies to procure service assurance under dynamic situations, without involving significant communication overhead. By adopting the concept of size threshold, the proposed technique continuously maintains the appropriate size of community in constantly and rapidly changing operating environment, to deliver optimal quality of service in terms of response time. The effectiveness of proposed technology has been shown through simulation, which reveals remarkable improvement (up to 29%) in response time.

The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

The automation of traceability links or traceability matrices is important to many software development paradigms. In turn, the efficiency and effectiveness of the recovery of traceability links in the distributed software development is becoming increasingly vital due to complexity of project developments, as this include continuous change in requirements, geographically dispersed project teams, and the complexity of managing the elements of a project - time, money, scope, and people. Therefore, the traceability links among the requirements artifacts, which fulfill business objectives, is also critical to reduce the risk and ensures project‘s success. This paper proposes Autonomous Decentralized Semantic based Traceability Link Recovery (AD-STLR) architecture. According to best of our knowledge this is the first architectural approach that uses an autonomous decentralized concept, DBpedia knowledge-base, Babelnet 2.5 multilingual dictionary and semantic network, for finding similarity among different project artifacts and the automation of traceability links recovery.

To enable the vision of precision medicine, evidence-based medicine is the key element. Understanding the natural history of complex diseases like brain aneurysm and particularly investigating the evidences of its rupture risk factors relies on the existence of semantic-enabled data preparation technology to conduct clinical trials, survival analysis and outcome prediction. For personalized medicine in the field of neurological diseases, it is very important that multiple health organizations coordinate and cooperate to conduct evidence based observational studies. Without the means of automating the process of privacy and semantic-enabled data preparation to conduct observational studies at intra-organizational level would require months to manually prepare the data. Therefore, this paper proposes a semantic and privacy enabled, multi-party data preparation architecture and a four-tiered semantic similarity algorithm. Evaluation shows that proposed algorithm achieves a precision of 79%, high recall at 83% and F-measure of 81%.