Liangcun Jiang

Towards intelligent GIServices

Distributed information infrastructures are increasingly used in the geospatial domain. In the infrastructures, data are being collected by distributed sensor services, served by distributed geospatial data services, transformed by processing services and workflows, and consumed by smart clients. Consequently, Geographical Information Systems (GISs) are moving from GISystems to GIServices. Intelligent GIServices are enriched with new capabilities including knowledge representation, semantic reasoning, automatic workflow composition, and quality and traceability. Such Intelligent GIServices facilitate information discovery and integration over the network and automate the assembly of GIServices to provide value-added products. This paper provides an overview of intelligent GIServices. The concept of intelligent GIServices is described, followed by a review of the state-of-the-art technologies and methodologies relevant to intelligent GIServices. Visions on how GIServices can perceive, reason, learn, and act intelligently are highlighted. The results can provide better services for big data processing, semantic interoperability, knowledge discovery, and cross-discipline collaboration in Earth science applications.

An SDI Approach for Big Data Analytics: The Case on Sensor Web Event Detection and Geoprocessing Workflow

In the big data era, scientific and social data could complement each other for enhanced data analysis and scientific discovery. Such capabilities could be achieved by taking an infrastructure-based approach, compared to existing algorithm-based approaches. This paper investigates how scientific and social data could work together in a spatial data infrastructure (SDI) enabled by interoperable services. It takes a human-as-sensor perspective and treats the social data as a special kind of sensor data, which could be mined and used for event detection in the Sensor Web environment. Sensor Web, social data mining, and geoprocessing workflows are combined together for timely decision support from social and sensor data. The result is an SDI approach for big data analytics. A use case on haze-related data mining and analysis illustrates the applicability of the approach.

Google Fusion Tables for Managing Soil Moisture Sensor Observations

Soil moisture plays a significant role in both water and energy cycles. It is important to manage and analyze in situ sensor observations of soil moisture due to its impacts on agricultural and hydrological processes. Google Fusion Tables (GFT) is a cloud computing database that provides a service on the Web for data management and integration. Using GFT for managing soil moisture sensor observations, it is possible to take advantages of GFT for collaborative management, on-the-fly visualization, and flexible integration and analysis. The Open Geospatial Consortium (OGC) sensor observation service (SOS) can provide real-time or near-real-time observations in an interoperable way. Combing SOS and GFT together can take the best of both. The paper investigates how GFT could be employed for managing, visualizing, and analyzing soil moisture sensor observations. It describes the design and implementation of a cloud-based SOS for managing soil moisture data using cloud computing databases. By storing sensor observations in GFT, the SOS service is scalable, and observations can be visualized and analyzed on demand. Challenges and approaches on the integration of GFT and SOS are discussed. A prototype service on sharing and managing soil moisture sensor observations is developed to demonstrate the applicability of the approach.

Recent Activities in Earth Data Science [Technical Committees]

Recent trends on big Earth-observing (EO) data lead to some questions that the Earth science community needs to address. Are we experiencing a paradigm shift in Earth science research now? How can we better utilize the explosion of technology maturation to create new forms of EO data processing? Can we summarize the existing methodologies and technologies scaling to big EO data as a new field named earth data science? Big data technologies are being widely practiced in Earth sciences and remote sensing communities to support EO data access, processing, and knowledge discovery. The data-intensive scientific discovery, named the fourth paradigm, leads to data science in the big data era [1]. According to the definition by the U.S. National Institute of Standards and Technology, the data science paradigm is the extraction of actionable knowledge directly from data through a process of discovery, hypothesis, and hypothesis testing [2]. Earth data science is the art and science of applying the data science paradigm to EO data.

Fuzzy spatial relation ontology driven detection of complex geospatial features in a web service environment

Remote sensing images have been widely used by intelligence analysts to discover geospatial features. The overwhelming volume of remote sensing imagery requires automated methods or systems for feature discovery. Existing research focuses on automatic extraction of isolated or elementary features, such as buildings and roads. It is rather understudied to discover complex geospatial features, which is spatially composed of elementary features. From the e-Science perspective, service computing technologies have shown great promise for widespread automation of data analysis and computation. The discovery of complex features would benefit from service computing technologies by computing spatial relations and their fuzziness among elementary features using geoprocessing services. The discovery process can be automated using an ontology approach. The paper presents how ontologies for complex geospatial features, enriched with fuzzy sets of spatial relations, can automate the workflow generation. Spatial computation functions, fuzzy membership functions, and mathematical fuzzy logical operators, are provided as services, and plugged into workflows on demand to enjoy the benefits of service computing technologies. A prototype system demonstrates on-demand uncertainty-aware detection of complex geospatial features in a geoprocessing service environment.

Task-oriented Sensor Web data processing for environmental monitoring

With the rapid development of geospatial service and sensor technologies, large volumes of geospatial data have been collected using various sensor networks, and accessible on the Web. Traditional geospatial data processing could be task-oriented, since a geoprocessing task can be described by a set of action steps, implemented as a workflow, and executed using distributed geoprocessing services. Tasks facilitate the expression of user requirements and capture the problem solving knowledge of users. In this paper, the task-oriented approach is extended to the OGC Sensor Web environment. It highlights how the event-driven technologies adopted by the Sensor Web can be leveraged with geoprocessing workflows to support environment monitoring tasks. The proposal of a Task Model Language (TaskML) and task trigger mechanism allows environmental events to be plugged into an existing model builder, GeoJModelBuilder. Tasks can be created in a stepwise manner, and their execution priority can be updated automatically using triggers. Compared to the traditional “reactive” task enactment mode, the trigger-augmented task can support “active” environmental monitoring. Use cases on PM2.5/PM10 monitoring demonstrate the applicability of the approach.

Sensor Web event detection and geoprocessing over Big data

In the Big Data era, scientific and social data can complement each other for enhanced data analysis and scientific discovery. In the geospatial domain, Sensor Web technologies can provide real-time or near real-time geospatial data to support timely decision-making. Scientific workflows have been identified as a key approach to support Big Data analytics for scientists. In the human-as-sensor aspect, the social content, as a special kind of sensor data, could be mined and fused in a Sensor Event Service (SES), allowing social data to be used inside a Spatial Data Infrastructure (SDI). This paper investigates an infrastructural approach on how social and scientific data could work together in an interoperable service environment. Sensor Web and geoprocessing services technologies are leveraged together for timely decision support from social and sensor data. A use case on haze-related data mining and analysis illustrates the applicability of the approach.

Semantic location-based services

Semantic location-based services (LBS) aims to provide intelligent LBS that can find and integrate various information to better meet user requirements in location-aware context. This paper argues that traditional approaches for semantic GIServices in the Cyberinfrastructure context could be extended into the LBS. After highlighting the distinguished features of semantic LBS, i.e. context semantics and location semantics, it suggests a common semantic framework that can accommodate semantics in both LBS and GIServices. A semantic-aware LBS architecture is proposed, which supports the context-aware discovery, access, composition, and use of Web information. A case study illustrates the applicability of the approach.

Extending PROV Data Model for Provenance-Aware Sensor Web

Provenance has become a fundamental issue in Sensor Web, since it allows applications to answer what, why, where, when, and how queries related to the consumption process, which finally helps to determine the usability and reliability of data products. This paper proposes how the W3C PROV Data Model PROV-DM [1] can be used for creating a lineage model for Sensor Web to support interoperability.

Advancing interoperability of geospatial data provenance on the web: Gap analysis and strategies

Abstract Geospatial data provenance is a fundamental issue in distributing spatial information on the Web. In the geoinformatics domain, provenance is often referred to as lineage. While the ISO 19115 lineage model is used widely in spatial data infrastructures, W3C has recommended the W3C provenance (PROV) data model for capturing and sharing provenance information on the Web. The use of these two separate efforts needs to be harmonized so that geospatial information does not remain an isolated area on the Web. Motivated by several domain use cases, we synthesize a list of provenance questions and analyze gaps between the two models in addressing these questions. Our strategy is to enrich W3C PROV with domain semantics from the ISO 19115 lineage model by suggesting ways to bridge them. A semantic mapping between the ISO lineage model and the W3C PROV model is formalized, and key issues involved are discussed. Use cases illustrate the applicability of the approach.