Satya S. Sahoo

Semantic Sensor Web

Sensors are distributed across the globe leading to an avalanche of data about our environment. It is possible today to utilize networks of sensors to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge. With a view to addressing this problem, the semantic sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.

Janus : From Workflows to Semantic Provenance and Linked Open Data

Data provenance graphs are form of metadata that can be used to establish a variety of properties of data products that undergo sequences of transformations, typically specified as workflows. Their usefulness for answering user provenance queries is limited, however, unless the graphs are enhanced with domain-specific annotations. In this paper we propose a model and architecture for semantic, domain-aware provenance, and demonstrate its usefulness in answering typical user queries. Furthermore, we discuss the additional benefits and the technical implications of publishing provenance graphs as a form of Linked Data. A prototype implementation of the model is available for data produced by the Taverna workflow system.

Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource.

ABSTRACT Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.

Knowledge modeling and its application in life sciences: a tale of two ontologies

High throughput glycoproteomics, similar to genomics and proteomics, involves extremely large volumes of distributed, heterogeneous data as a basis for identification and quantification of a structurally diverse collection of biomolecules. The ability to share, compare, query for and most critically correlate datasets using the native biological relationships are some of the challenges being faced by glycobiology researchers. As a solution for these challenges, we are building a semantic structure, using a suite of ontologies, which supports management of data and information at each step of the experimental lifecycle. This framework will enable researchers to leverage the large scale of glycoproteomics data to their benefit.In this paper, we focus on the design of these biological ontology schemas with an emphasis on relationships between biological concepts, on the use of novel approaches to populate these complex ontologies including integrating extremely large datasets ( 500MB) as part of the instance base and on the evaluation of ontologies using OntoQA [38] metrics. The application of these ontologies in providing informatics solutions, for high throughput glycoproteomics experimental domain, is also discussed. We present our experience as a use case of developing two ontologies in one domain, to be part of a set of use cases, which are used in the development of an emergent framework for building and deploying biological ontologies.

Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care.

OBJECTIVE Epilepsy encompasses an extensive array of clinical and research subdomains, many of which emphasize multi-modal physiological measurements such as electroencephalography and neuroimaging. The integration of structured, unstructured, and signal data into a coherent structure for patient care as well as clinical research requires an effective informatics infrastructure that is underpinned by a formal domain ontology. METHODS We have developed an epilepsy and seizure ontology (EpSO) using a four-dimensional epilepsy classification system that integrates the latest International League Against Epilepsy terminology recommendations and National Institute of Neurological Disorders and Stroke (NINDS) common data elements. It imports concepts from existing ontologies, including the Neural ElectroMagnetic Ontologies, and uses formal concept analysis to create a taxonomy of epilepsy syndromes based on their seizure semiology and anatomical location. RESULTS EpSO is used in a suite of informatics tools for (a) patient data entry, (b) epilepsy focused clinical free text processing, and (c) patient cohort identification as part of the multi-center NINDS-funded study on sudden unexpected death in epilepsy. EpSO is available for download at http://prism.case.edu/prism/index.php/EpilepsyOntology. DISCUSSION An epilepsy ontology consortium is being created for community-driven extension, review, and adoption of EpSO. We are in the process of submitting EpSO to the BioPortal repository. CONCLUSIONS EpSO plays a critical role in informatics tools for epilepsy patient care and multi-center clinical research.

Heart beats in the cloud: distributed analysis of electrophysiological ‘Big Data’ using cloud computing for epilepsy clinical research

OBJECTIVE The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. MATERIALS AND METHODS We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. RESULTS Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. DISCUSSION Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. CONCLUSION The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

Significant postictal hypotension: expanding the spectrum of seizure-induced autonomic dysregulation.

Periictal autonomic dysregulation is best studied using a “polygraphic” approach: electroencephalography ([EEG]), 3‐channel electrocardiography [ECG], pulse oximetry, respiration, and continuous noninvasive blood pressure [BP]), which may help elucidate agonal pathophysiologic mechanisms leading to sudden unexpected death in epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic–clonic seizures (GTCS), the most common seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias, and changes in skin conductance. Postictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP, and PGES has been found to correlate with post‐GTCS autonomic dysregulation in some patients. Herein, we describe a patient with a GTCS in whom polygraphic measurements were obtained, including continuous noninvasive blood pressure recordings. Significant postictal hypotension lasting >60 s was found, which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon, and consequent cerebral hypoperfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged, and irrecoverable hypotension may comprise one potential SUDEP mechanism.

Template based semantic similarity for security applications

Todays search technology delivers impressive results in finding relevant documents for given keywords. However many applications in various fields including genetics, pharmacy, social networks, etc. as well as national security need more than what traditional search can provide. Users need to query a very large knowledge base (KB) using semantic similarity, to discover its relevant subsets. One approach is to use templates that support semantic similarity-based discovery of suspicious activities, that can be exploited to support applications such as money laundering, insider threat and terrorist activities. Such discovery that relies on a semantic similarity notion will tolerate syntactic differences between templates and KB using ontologies. We address the problem of identifying known scenarios using a notion of template-based similarity performed as part of the SemDIS project [1, 3]. This approach is prototyped in a system named TRAKS (Terrorism Related Assessment using Knowledge Similarity) and tested using scenarios involving potential money laundering.

Domain Ontology As Conceptual Model for Big Data Management: Application in Biomedical Informatics

The increasing capability and sophistication of biomedical instruments has led to rapid generation of large volumes of disparate data that is often characterized as biomedical “big data”. Effective analysis of biomedical big data is providing new insights to advance healthcare research, but it is difficult to efficiently manage big data without a conceptual model, such as ontology, to support storage, query, and analytical functions. In this paper, we describe the Cloudwave platform that uses a domain ontology to support optimal data partitioning, efficient network transfer, visualization, and querying of big data in the neurology disease domain. The domain ontology is used to define a new JSON-based Cloudwave Signal Format (CSF) for neurology signal data. A comparative evaluation of the ontology-based CSF with existing data format demonstrates that it significantly reduces the data access time for query and visualization of large scale signal data.

Extending Semantic Provenance into the Web of Data

In this article, the authors provide an example workflow-and a simple classification of user questions on the workflows data products-to combine and interchange contextual metadata through a semantic data model and infrastructure. They also analyze their approachs potential to support enhanced semantic provenance applications.