15,506 research outputs found
Explanations Generation For Web Service Workflow
The motivation for the work is the challenge of providing textual explanations of automatically generated scientific workflows (e.g., paragraphs that scientists can include in their publications).
The extended abstract presents a system which generates explanations for a web service workflow from sets of atoms derived from a collection of ontologies. The system, called nlgPhylogeny, demonstrates the feasibility of the task in the Phylotastic project, that aims at providing evolutionary biologists with a platform for automatic generation of phylogenetic trees given some suitable inputs
From usability to secure computing and back again
Secure multi-party computation (MPC) allows multiple parties
to jointly compute the output of a function while preserving
the privacy of any individual partyâs inputs to that function.
As MPC protocols transition from research prototypes to realworld
applications, the usability of MPC-enabled applications
is increasingly critical to their successful deployment and
widespread adoption. Our Web-MPC platform, designed with
a focus on usability, has been deployed for privacy-preserving
data aggregation initiatives with the City of Boston and the
Greater Boston Chamber of Commerce. After building and
deploying an initial version of the platform, we conducted a
heuristic evaluation to identify usability improvements and
implemented corresponding application enhancements. However,
it is difficult to gauge the effectiveness of these changes
within the context of real-world deployments using traditional
web analytics tools without compromising the security guarantees
of the platform. This work consists of two contributions
that address this challenge: (1) the Web-MPC platform has
been extended with the capability to collect web analytics
using existing MPC protocols, and (2) as a test of this feature
and a way to inform future work, this capability has been
leveraged to conduct a usability study comparing the two versions
ofWeb-MPC. While many efforts have focused on ways
to enhance the usability of privacy-preserving technologies,
this study serves as a model for using a privacy-preserving
data-driven approach to evaluate and enhance the usability of
privacy-preserving websites and applications deployed in realworld
scenarios. Data collected in this study yields insights
into the relationship between usability and security; these can
help inform future implementations of MPC solutions.Published versio
What Automated Planning Can Do for Business Process Management
Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
The Semantic Grid: A future e-Science infrastructure
e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practiceâaspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid
Natural Language Generation From Ontologies Using Grammatical Framework
The paper addresses the problem of automatic generation of natural language descriptions for ontology-described artifacts. The motivation for the work is the challenge of providing textual descriptions of automatically generated scientific workflows (e.g., paragraphs that scientists can include in their publications). The extended abstract presents a system which generates descriptions of sets of atoms derived from a collection of ontologies. The system, called nlgPhylogeny, demonstrates the feasibility of the task in the Phylotastic project, that aims at providing evolutionary biologists with a platform for automatic generation of phylogenetic trees given some suitable inputs. nlgPhylogeny utilizes the fact that the Grammatical Framework (GF) is suitable for the natural language generation (NLG) task; the abstract shows how elements of the ontologies in Phylotastic, such as web services, inputs and outputs of web services, can be encoded in GF for the NLG task
ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)
We present ExplainIt!, a declarative, unsupervised root-cause analysis engine
that uses time series monitoring data from large complex systems such as data
centres. ExplainIt! empowers operators to succinctly specify a large number of
causal hypotheses to search for causes of interesting events. ExplainIt! then
ranks these hypotheses, reducing the number of causal dependencies from
hundreds of thousands to a handful for human understanding. We show how a
declarative language, such as SQL, can be effective in declaratively
enumerating hypotheses that probe the structure of an unknown probabilistic
graphical causal model of the underlying system. Our thesis is that databases
are in a unique position to enable users to rapidly explore the possible causal
mechanisms in data collected from diverse sources. We empirically demonstrate
how ExplainIt! had helped us resolve over 30 performance issues in a commercial
product since late 2014, of which we discuss a few cases in detail.Comment: SIGMOD Industry Track 201
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