The lab of Atul Butte builds and applies computational tools that convert more than 300 billion points of molecular, clinical, and epidemiological data-measured by researchers and clinicians over the past decade-into new diagnostics, therapeutics, and novel insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his team's recent work on clinical evaluations of patients presenting with personal genomes, enabled by the largest curated database of human disease-associated SNP's. With so many genomes now sequenced from individuals from a variety of ethnic backgrounds, and analyzed in a clinical context, Dr. Butte will present how ethnicity alters the background distribution of disease SNP's. Finally, Dr. Butte will also present his team's recent work on environment-wide association studies (EWAS) and how they enable studies of gene-environment interactions.

Functionality makes APIs unique and therefore helps humans and machines decide what service they need. However, if two APIs offer similar functionality, quality attributes such as performance and ease-of-use might become a decisive factor. Several of these quality attributes are inherently subjective, and hence exist within a social context. These social parameters should be taken into account when creating personalized mashups and service compositions. The Web API description format RESTdesc already captures functionality in an elegant way, so in this paper we will demonstrate how it can be extended to include social parameters. We indicate the role these parameters can play in generating functional compositions that fulfill specified quality attributes. Finally, we show how descriptions can be personalized by exploring a user’s social graph. This ultimately leads to a more focused, on-demand use of Web APIs, driven by functionality and social parameters.

The Web is a giant repository of resources (Service and content), where Discovery and Recommendation systems are used to deliver the best ranked list of relevant web resources that meet user requirements. Nowadays, these systems are based on the simulation and automation of the user search criteria, considering the relation between consumption trends and the different kinds of users’ relationships with their virtual and physical environment, based on the information from the Social Web and mobile device sensors among others. These systems are executed once an explicit query of the user has been received; however, there are resources that are useful in specific situations, where these resources have high probability to be consumed, but, due to absence of a query they are not recommended to the users. In this regard, the question is: how to make a successful Web Resource Recommendation without the user query? In order to answer the question, this research proposal presents a novel approach to Recommend Web Resources based on Dynamic Prediction of User Consumption on the Social Web, which emulates the user behavior, the resource dynamism and the context opportunities, in real time, catching the best situations to make an asynchronous (unexpected by the user) recommendation of a useful Resources; and boost Web Resources consumption.

In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

The concept of trust in web services describes the degree of belief that a client or a group of clients have over services functioning satisfactorily and providing the expected results. As services are usually invoked in composition with other services, judging on their trustworthiness gets more complicated, yet computing their trustworthy becomes a desired goal. Existing work only take the trust of each individual service into account, regardless of the context of the composition. They also do not use the data gained from other clients for selecting the most trustful composition and preparing for possible service failures. In our work we first introduce the concept of Combination Reputation, which reflects the commonness and popularity of invoaction of a pair or group of services among other clients. By interpreting the trust and reputation values as subjective probability, we define the Component Trust of the services in the composition, which reflects the degree of belief the client has over components of services performing satisfactorily. We model the web service composition as a Bayesian network and integrate the above trust values into the network and show how to compute the global trust of the composition.

Existing personalisation approaches, such as collaborative filtering or content based recommendations, are highly dependent on the domain and/or the source of the data. Therefore, there is a need for more accurate means to capture and model the interests of the user across domains, and to interlink them in a semantically-enhanced interest graph. We propose a new approach for multi-source, cross-genre recommendations that can exploit the heterogeneous nature of user profile data, which has been aggregated from multiple personalised web services, such as blogs, wikis and microblogs. Our approach is based on the Spreading Activation model that exploits intrinsic links between entities across a number of data sources. The proposed method is highly customizable and applicable both to generic and specific recommendation scenarios and use cases. With the growing number of Social Web applications in the enterprise (blogs, wikis, micro blogging, etc.), it becomes difficult for knowledge workers to avoid content overload and to quickly identify relevant people, communities and information. We demonstrate the application of our approach in an industrial use case that involves recommendation of social semantic data across multiple services in a distributed collaborative environment.

Service composition, which achieves the goal of value-added services, has been considered as the core technique of Service-oriented Computing (SOC). To cope with the challenge of ever-increasing number of web services, graph-based web service network has emerged as a potential solution to the state of art SOC. In such a way, composite services are constructed by applying searching algorithms to the built graph, and proved to achieve outstanding performance in complexity. However, web service network suffers two crucial disadvantages: poor connectivity and negative links, and both of them have crucial negative impact on service composition. To cope with the problems, we propose two methods in this paper. Firstly, leveraging social network analysis, we focus on enriching web service network by adding valuable services, which will play positive roles in solving poor connective problem. Secondly, we show a serious status that numerous negative links contained in the underlying networks, and then we propose to identify and remove the negative links based on users’ historical composite services.

Recently, online vendors and providers manage review systems as a mechanism to advertise their services and goods over the Web. In making their choice, clients can rely on feedback expressing the degree of satisfaction of past users with respect to such services and goods. This set of feedback, or reviews, may be filtered by categories of users, they may be affected by multiple factors, and they are elaborated in order to obtain an overall score, representing a global indicator aimed at summarising the level of quality of that service. In this paper, we concentrate on multi-factor review,~\ie a review whose value is computed assembling the scores given to a set of parameters that may affect the quality level of a service. Our interest is evaluating the relevance, or dominance, of some parameter with respect to the others. We advocate the use of the Analytic Hierarchy Process, a well-known technique born in the area of multi-criteria decision making, to derive the priorities to assign to the scores of the single parameters. We illustrate the proposal on the example of hotel reviews.

Service discovery and composition within the ProgrammableWeb directory is a difficult process, since it requires considerable manual effort to locate services, understand their capabilities and compose mashup applications. Furthermore, every site has its databases modeled in a specific way, causing semantically equivalent properties to be defined differently, since data is not easily shared across different domains in the Internet. With the use of Semantic Web technologies, such as description logic ontologies and reasoners to describe Web Services, automated service discovery and composition as well as data linking are made possible. Currently, Programmable Web classifies APIs in a flat categorization where each API is manually classified within a single service category. Search is limited to attributes such as protocol or messaging type and is not related to semantic attributes of the service category. We enhance the service descriptions by using an ontology to describe the domain of each service category. With an ontology description, an API can be automatically classified and queried for according to its attributes. Additionally, APIs can be distributed in ontology-based service discovery systems so that semantic registration and querying of services become possible. One of the limitations in using ontologies for describing a service domain is in creating its generic description. Current work in creating domain ontologies is limited to semi-automated ontology generation tools which create pure hierarchical classifications, given a well-defined corpus or taxonomy, but do not include property descriptions. We propose LexOnt, a semi-automatic ontology creation tool for a high-level service classification ontology. We use the PW directory as the corpus, although it may be used for other corpuses as well. The main contribution of LexOnt is its novel algorithm which generates and ranks frequent terms and significant phrases within a PW category by comparing them to external domain knowledge within Wikipedia, Wordnet and the current state of the ontology. First it matches terms to the Wikipedia page description of the category and ranks them higher, since these indicate domain descriptive words. Synonymous words from Wordnet are then matched and ranked. In a semi-automated process, the user chooses the terms it wants to add to the ontology and indicates the properties to assign these values to and the ontology is automatically generated. In the next iteration, terms within the current state of the ontology are compared to terms in the other categories and automatic property assignments are made for these API instances as well.

Defender-Attacker Stackelberg games are the foundations of toolsdeployed for computing optimal patrolling strategies in adversarialdomains such as the United states Federal Air Marshals Service and the UnitedStates Coast Guard, among others.In Stackelberg game models of these systems the attacker knows only theprobability that each target is covered by the defender, but isoblivious to the detailed timing of the coverage schedule.In many real-world situations, however, the attacker can observe thecurrent location of the defender and can exploit this knowledge toreason about the defender's future moves.We study Stackelberg security games in which the defender sequentiallymoves between targets, with moves constrained by an exogenouslyspecified graph, while the attacker can observe the defender's currentlocation and his (stochastic) policy concerning future moves. We offerfive contributions: (1) We model this adversarial patrolling game (APG) as a stochastic game with special structure and presentseveral alternative formulations that leverage the general non-linearprogramming (NLP) approach for computing equilibria in zero-sumstochastic games. We show that our formulations yield significantlybetter solutions than previous approaches. (2) We extend theNLP formulation for APG allow for attacks that may take multiple timesteps to unfold.(3) We provide anapproximate MILP formulation that uses discrete defender moveprobabilities. (4) We experimentally demonstrate the efficacy of anNLP-based approach, and systematically study the impact of networktopology on the results.(5) We extend our model to allow the defender to construct the graph constraining his moves, at some cost, and offer novel algorithms for this setting, finding that a MILP approximation is much more effective than the exact NLP in this setting.