6,256 research outputs found
Abstract State Machines 1988-1998: Commented ASM Bibliography
An annotated bibliography of papers which deal with or use Abstract State
Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm
Research on knowledge representation, machine learning, and knowledge acquisition
Research in knowledge representation, machine learning, and knowledge acquisition performed at Knowledge Systems Lab. is summarized. The major goal of the research was to develop flexible, effective methods for representing the qualitative knowledge necessary for solving large problems that require symbolic reasoning as well as numerical computation. The research focused on integrating different representation methods to describe different kinds of knowledge more effectively than any one method can alone. In particular, emphasis was placed on representing and using spatial information about three dimensional objects and constraints on the arrangement of these objects in space. Another major theme is the development of robust machine learning programs that can be integrated with a variety of intelligent systems. To achieve this goal, learning methods were designed, implemented and experimented within several different problem solving environments
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Exploring data requires a fast feedback loop from the analyst to the system,
with a latency below about 10 seconds because of human cognitive limitations.
When data becomes large or analysis becomes complex, sequential computations
can no longer be completed in a few seconds and data exploration is severely
hampered. This article describes a novel computation paradigm called
Progressive Computation for Data Analysis or more concisely Progressive
Analytics, that brings at the programming language level a low-latency
guarantee by performing computations in a progressive fashion. Moving this
progressive computation at the language level relieves the programmer of
exploratory data analysis systems from implementing the whole analytics
pipeline in a progressive way from scratch, streamlining the implementation of
scalable exploratory data analysis systems. This article describes the new
paradigm through a prototype implementation called ProgressiVis, and explains
the requirements it implies through examples.Comment: 10 page
Knowledge Representation Concepts for Automated SLA Management
Outsourcing of complex IT infrastructure to IT service providers has
increased substantially during the past years. IT service providers must be
able to fulfil their service-quality commitments based upon predefined Service
Level Agreements (SLAs) with the service customer. They need to manage, execute
and maintain thousands of SLAs for different customers and different types of
services, which needs new levels of flexibility and automation not available
with the current technology. The complexity of contractual logic in SLAs
requires new forms of knowledge representation to automatically draw inferences
and execute contractual agreements. A logic-based approach provides several
advantages including automated rule chaining allowing for compact knowledge
representation as well as flexibility to adapt to rapidly changing business
requirements. We suggest adequate logical formalisms for representation and
enforcement of SLA rules and describe a proof-of-concept implementation. The
article describes selected formalisms of the ContractLog KR and their adequacy
for automated SLA management and presents results of experiments to demonstrate
flexibility and scalability of the approach.Comment: Paschke, A. and Bichler, M.: Knowledge Representation Concepts for
Automated SLA Management, Int. Journal of Decision Support Systems (DSS),
submitted 19th March 200
Data Management in Microservices: State of the Practice, Challenges, and Research Directions
We are recently witnessing an increased adoption of microservice
architectures by the industry for achieving scalability by functional
decomposition, fault-tolerance by deployment of small and independent services,
and polyglot persistence by the adoption of different database technologies
specific to the needs of each service. Despite the accelerating industrial
adoption and the extensive research on microservices, there is a lack of
thorough investigation on the state of the practice and the major challenges
faced by practitioners with regard to data management. To bridge this gap, this
paper presents a detailed investigation of data management in microservices.
Our exploratory study is based on the following methodology: we conducted a
systematic literature review of articles reporting the adoption of
microservices in industry, where more than 300 articles were filtered down to
11 representative studies; we analyzed a set of 9 popular open-source
microservice-based applications, selected out of more than 20 open-source
projects; furthermore, to strengthen our evidence, we conducted an online
survey that we then used to cross-validate the findings of the previous steps
with the perceptions and experiences of over 120 practitioners and researchers.
Through this process, we were able to categorize the state of practice and
reveal several principled challenges that cannot be solved by software
engineering practices, but rather need system-level support to alleviate the
burden of practitioners. Based on the observations we also identified a series
of research directions to achieve this goal. Fundamentally, novel database
systems and data management tools that support isolation for microservices,
which include fault isolation, performance isolation, data ownership, and
independent schema evolution across microservices must be built to address the
needs of this growing architectural style
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