37,379 research outputs found
Monitoring and analysis of data from complex systems
Some of the methods, systems, and prototypes that have been tested for monitoring and analyzing the data from several spacecraft and vehicles at the Marshall Space Flight Center are introduced. For the Huntsville Operations Support Center (HOSC) infrastructure, the Marshall Integrated Support System (MISS) provides a migration path to the state-of-the-art workstation environment. Its modular design makes it possible to implement the system in stages on multiple platforms without the need for all components to be in place at once. The MISS provides a flexible, user-friendly environment for monitoring and controlling orbital payloads. In addition, new capabilities and technology may be incorporated into MISS with greater ease. The use of information systems technology in advanced prototype phases, as adjuncts to mainline activities, is used to evaluate new computational techniques for monitoring and analysis of complex systems. Much of the software described (specially, HSTORESIS (Hubble Space Telescope Operational Readiness Expert Safemode Investigation System), DRS (Device Reasoning Shell), DART (Design Alternatives Rational Tool), elements of the DRA (Document Retrieval Assistant), and software for the PPS (Peripheral Processing System) and the HSPP (High-Speed Peripheral Processor)) is available with supporting documentation, and may be applicable to other system monitoring and analysis applications
Advanced language modeling approaches, case study: Expert search
This tutorial gives a clear and detailed overview of advanced language modeling approaches and tools, including the use of document priors, translation models, relevance models, parsimonious models and expectation maximization training. Expert search will be used as a case study to explain the consequences of modeling assumptions
Capturing flight system test engineering expertise: Lessons learned
Within a few years, JPL will be challenged by the most active mission set in history. Concurrently, flight systems are increasingly more complex. Presently, the knowledge to conduct integration and test of spacecraft and large instruments is held by a few key people, each with many years of experience. JPL is in danger of losing a significant amount of this critical expertise, through retirement, during a period when demand for this expertise is rapidly increasing. The most critical issue at hand is to collect and retain this expertise and develop tools that would ensure the ability to successfully perform the integration and test of future spacecraft and large instruments. The proposed solution was to capture and codity a subset of existing knowledge, and to utilize this captured expertise in knowledge-based systems. First year results and activities planned for the second year of this on-going effort are described. Topics discussed include lessons learned in knowledge acquisition and elicitation techniques, life-cycle paradigms, and rapid prototyping of a knowledge-based advisor (Spacecraft Test Assistant) and a hypermedia browser (Test Engineering Browser). The prototype Spacecraft Test Assistant supports a subset of integration and test activities for flight systems. Browser is a hypermedia tool that allows users easy perusal of spacecraft test topics. A knowledge acquisition tool called ConceptFinder which was developed to search through large volumes of data for related concepts is also described and is modified to semi-automate the process of creating hypertext links
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
Development of an intelligent hypertext manual for the space shuttle hazardous gas detection system
A computer-based Integrated Knowledge System (IKS), the Intelligent Hypertext Manual (IHM), is being developed for the Space Shuttle Hazardous Gas Detection System (HGDS) at the Huntsville Operations Support Center (HOSC). The IHM stores all HGDS related knowledge and presents them in an interactive and intuitive manner. The IHM's purpose is to provide HGDS personnel with the capabilities of: enhancing the interpretation of real time data; recognizing and identifying possible faults in the Space Shuttle sub-system related to hazardous gas detections; locating applicable documentation related to procedures, constraints, and previous fault histories; and assisting in the training of personnel
Toward an expert project management system
The purpose of the research effort is to prescribe a generic reusable shell that any project office can install and customize for the purposes of advising, guiding, and supporting project managers in that office. The prescribed shell is intended to provide both: a component that generates prescriptive guidance for project planning and monitoring activities, and an analogy (intuition) component that generates descriptive insights of previous experience of successful project managers. The latter component is especially significant in that it has the potential to: retrieve insights, not just data, and provide a vehicle for expert PMs to easily transcribe their current experiences in the course of each new project managed
- …