23,865 research outputs found
The Role of Structural Reflection in Distributed Virtual Reality
The emergence of collaborative virtual world applications that run over the Internet has presented Virtual Reality (VR) application designers with new challenges. In an environment where the public internet streams multimedia data and is constantly under pressure to deliver over widely heterogeneous user-platforms, there has been a growing need that distributed virtual world applications be aware of and adapt to frequent variations in their context of execution. In this paper, we argue that in contrast to research efforts targeted at improvement of scalability, persistence and responsiveness capabilities, much less attempts have been aimed at addressing the flexibility, maintainability and extensibility requirements in contemporary Distributed VR applications. We propose the use of structural reflection as an approach that not only addresses these requirements but also offers added value in the form of providing a framework for scalability, persistence and responsiveness that is itself flexible, maintainable and extensible
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, causal
models of the same system should be consistent with one another in the sense
that they agree in their predictions of the effects of interventions. We
formalise this notion of consistency in the case of Structural Equation Models
(SEMs) by introducing exact transformations between SEMs. This provides a
general language to consider, for instance, the different levels of description
in the following three scenarios: (a) models with large numbers of variables
versus models in which the `irrelevant' or unobservable variables have been
marginalised out; (b) micro-level models versus macro-level models in which the
macro-variables are aggregate features of the micro-variables; (c) dynamical
time series models versus models of their stationary behaviour. Our analysis
stresses the importance of well specified interventions in the causal modelling
process and sheds light on the interpretation of cyclic SEMs.Comment: equal contribution between Rubenstein and Weichwald; accepted
manuscrip
An approach to relate business and application services using ISDL
This paper presents a service-oriented design approach that allows one to relate services modelled at different levels of granularity during a design process, such as business and application services. To relate these service models we claim that a 'concept gap' and an 'abstraction gap' need to be bridged. The concept gap represents the difference between the conceptual models used to construct service models by different stakeholders involved in the design process. The abstraction gap represents the difference in abstraction level at which service models are defined. Two techniques are presented that bridge these gaps. Both techniques are based on the Interaction System Design Language (ISDL). The paper illustrates the use of both techniques through an example
Scalability approaches for causal multicast: a survey
The final publication is available at Springer via http://dx.doi.org/10.1007/s00607-015-0479-0Many distributed services need to be scalable: internet search,
electronic commerce, e-government... In order to
achieve scalability, high availability and fault tolerance, such
applications rely on replicated components. Because of the dynamics
of growth and volatility of customer markets, applications need to be
hosted by adaptive, highly scalable systems. In particular, the
scalability of the reliable multicast mechanisms used for supporting
the consistency of replicas is of crucial importance. Reliable
multicast might propagate updates in a pre-determined order (e.g.,
FIFO, total or causal). Since total order needs more communication
rounds than causal order, the latter appears to be the preferable
candidate for achieving multicast scalability, although the
consistency guarantees based on causal order are weaker than those of
total order. This paper provides a historical survey of different
scalability approaches for reliable causal multicast protocols.This work was supported by European Regional Development Fund (FEDER) and Ministerio de Economia y Competitividad (MINECO) under research Grant TIN2012-37719-C03-01.Juan MarĂn, RD.; Decker, H.; ArmendĂĄriz Ăñigo, JE.; Bernabeu AubĂĄn, JM.; Muñoz EscoĂ, FD. (2016). Scalability approaches for causal multicast: a survey. Computing. 98(9):923-947. https://doi.org/10.1007/s00607-015-0479-0S923947989Adly N, Nagi M (1995) Maintaining causal order in large scale distributed systems using a logical hierarchy. In: IASTED Intnl Conf on Appl Inform, pp 214â219Aguilera MK, Chen W, Toueg S (1997) Heartbeat: a timeout-free failure detector for quiescent reliable communication. In: 11th Intnl Wshop on Distrib Alg (WDAG), SaarbrĂŒcken, pp 126â140Almeida JB, Almeida PS, Baquero C (2004) Bounded version vectors. In: 18th Intnl Conf Distrib Comput (DISC), Amsterdam, pp 102â116Almeida PS, Baquero C, Fonte V (2008) Interval tree clocks. 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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
Principal Patterns on Graphs: Discovering Coherent Structures in Datasets
Graphs are now ubiquitous in almost every field of research. Recently, new
research areas devoted to the analysis of graphs and data associated to their
vertices have emerged. Focusing on dynamical processes, we propose a fast,
robust and scalable framework for retrieving and analyzing recurring patterns
of activity on graphs. Our method relies on a novel type of multilayer graph
that encodes the spreading or propagation of events between successive time
steps. We demonstrate the versatility of our method by applying it on three
different real-world examples. Firstly, we study how rumor spreads on a social
network. Secondly, we reveal congestion patterns of pedestrians in a train
station. Finally, we show how patterns of audio playlists can be used in a
recommender system. In each example, relevant information previously hidden in
the data is extracted in a very efficient manner, emphasizing the scalability
of our method. With a parallel implementation scaling linearly with the size of
the dataset, our framework easily handles millions of nodes on a single
commodity server
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