Vast volumes of data are continuously generated in smart oilfields from swarms of sensors. On one hand, increasing amounts of such data are stored in large data repositories and accessed over high-speed networks; On the other hand, captured data is further processed by different users in various analysis, prediction and domain-specific procedures that result in even larger volumes of derived datasets. The decision making process in smart oilfields relies on accurate historical, real-time or predicted datasets. However, the difficulty in searching for the right data mainly lies in the fact that data is stored in large repositories carrying no metadata to describe them. The origin or context in which the data was generated cannot be traced back, thus any meaning associated with the data is lost. Integrated views of data are required to make important decisions efficiently and effectively, but are difficult to produce; since data is being generated and stored in the repository may have different formats and schemata pertaining to different vendor products. In this paper, we present an approach based on Semantic Web Technologies that enables automatic annotation of input data with missing metadata, with terms from a domain ontology, which constantly evolves supervised by domain experts. We provide an intuitive user interface for annotation of datasets originating from the seismic image processing workflow. Our datasets contain models and different versions of images obtained from such models, generated as part of the oil exploration process in the oil industry. Our system is capable of annotating models and images with missing metadata, preparing them for integration by mapping such annotations. Our technique is abstract and may be used to annotate any datasets with missing metadata, derived from original datasets. The broader significance of this work is in the context of knowledge capturing, preservation and management for smart oilfields. Specifically our work focuses on extracting domain knowledge into collaboratively curated ontologies and using this information to assist domain experts in seamless data integration.

Seismic processing and interpretation involves resource intensive processing in the petroleum exploration domain. By employing various types of models, seismic interpretations are often derived in an iterative refinement process, which may result in multiple versions of seismic images. Keeping track of the derivation history (a.k.a. provenance) for such images thus becomes an important issue for data management. Specifically, the information about what velocity model was used to generate a seismic image is useful evidence for measuring the quality of the image. The information can also be used for audit trail and image reproduction. However, in practice, existing seismic processing and interpretation systems do not always automatically capture and maintain this type of provenance information. In this paper, by employing state-of-the-art techniques in text analytics, semantic processing and machine learning, we propose an approach that recovers the linkage between seismic images and their ancestral velocity models when no provenance information is recorded. Our approach first retrieves information from file/directory names of the images and models, such as project names, processing vendors, and algorithms involved in the seismic processing and interpretation. Along with the creation timestamps, the retrieved information is associated with corresponding images and models as metadata. The metadata of a seismic image and its ancestral models usually satisfy certain relationships. In our approach, we detect and represent such relationships as rules, and a matching process utilizes the rules and retrieved metadata to find the best-matching images and models. In practice, images and models file names often do not adhere to naming standards and they are stored without following well established record keeping practices. Users may also use different terms to express the same information in file/directory names. We employ Semantic Web technologies to address this challenge. We develop domain ontologies with OWL/RDFs, based on which we provide an interactive way for users to semantically annotate terms contained in file/directory names. All metadata used by the image-model matching process is represented as ontology instances. Matching can be performed using the standard semantic query language. The evaluation results show that our approach can achieve satisfying accuracy.

This paper presents a comprehensive study of the state of the art in Social Networking Analysis and examines the impact of content analysis and the effects of semantics in social networking analysis research. We propose a taxonomy of current approaches, classifying them into the following main categories: 1) graph-theoretic approaches, 2) applications of semantic web technologies and emergent semantics modeling, and 3) data mining and analytics. The purpose is to increase awareness of the social networking analysis community about different ongoing efforts, which not only focus on the network aspect of social networks, shed some light into different approaches and advance the discussion about potential future directions.

We present a formal Semantic Model, called social space, for Social Networks based on Vector Spaces. Our approach takes into account both the social data (captured in the profile, user posts, and user activity) and the network aspect (captured by the connections). Rather than saying that Alice is a friend of Bob, our model allows quantification of the strength of connection across different dimensions. For example, one can answer the question, "How close are Alice and Bob in the domain of sci-fi movies?" We also present new operators that use our model for computing distance and creating neighborhoods. We present the theoretical foundations of our model and outline its usability by demonstrating a targeted content delivery application on top of the Facebook platform. In this demonstration we use existing semantic models from the Linked Data Cloud for semantic analysis of social networking data.

Simulation models are commonly used as an aid to decision making for oilfield development and operations. Such models represent information uncertainty and alternate operational strategies and form a design space, which is used by engineers to explore different "what-if" scenarios. As the composition of the engineering team changes over the lifetime of the oilfield and new modeling requirements emerge, it becomes important for engineers to be able to quickly "mine" from and understand the distribution of the models in the design space, and also to know if a particular scenario was already modeled in the past or if a new model needs to be created. In this paper, we describe a technique to analyze arbitrarily large set of simulation models, identify similarities and differences between model parameters, and automatically cluster the models based on similarity in an n-dimensional design space. The major contribution of this work is a vector space based approach for automatic model clustering without human intervention. Building on this contribution, our system provides a smart browse, search and visualization capability over a legacy model catalog in a non-proprietary manner. The system also performs automatic analysis of available models in order to discover their underlying basic structure and, if possible, represents the models as variations of this basic structure. We demonstrate the application of our algorithm to a set of Integrated Production Modeling (IPM) models. However, our use of a standard, non-proprietary network model abstraction as an intermediate representation means that our analysis technique can be applied to models created using a variety of modeling and simulation tools. The broader significance of this work is in the context of knowledge management for smart oilfields, specifically focused on extracting meaningful information from legacy simulation models, and making this information available and useful to the domain experts.

Peer-to-peer networks have become extremely popular over the last decades for one major reason. They make the exchange of files between users easy and fast. Peer-to-peer systems have been proved to be the basis for the creation of worldwide networks where users exchange read-only files freely and efficiently. However, users have started using applications that need more than what the exchange of immutable files can offer. Collaborative Wikis have emerged as a phenomenon of people's behavior to communicate and collaboratively create and maintain large collections of documents. Communities that do not want to rely on centralized servers and legacy applications would benefit from using peer-to-peer systems for their purposes. The immutability of files that peer-to-peer systems can support however, cannot possibly serve this purpose. This paper makes three contributions towards the advances in theoretical foundations of P2P. It analyzes existing peer-to-peer systems supporting mutable files and indicates their successes and failures in this direction. It describes the characteristics that a mutable peer-to-peer file system should have in order to be able to support applications with characteristics of collaborative Wikis. Finally, it proposes an architecture for the creation of a new peer-to-peer system with the desirable characteristics, based on already existing systems.