452 research outputs found
Lightweight Asynchronous Snapshots for Distributed Dataflows
Distributed stateful stream processing enables the deployment and execution
of large scale continuous computations in the cloud, targeting both low latency
and high throughput. One of the most fundamental challenges of this paradigm is
providing processing guarantees under potential failures. Existing approaches
rely on periodic global state snapshots that can be used for failure recovery.
Those approaches suffer from two main drawbacks. First, they often stall the
overall computation which impacts ingestion. Second, they eagerly persist all
records in transit along with the operation states which results in larger
snapshots than required. In this work we propose Asynchronous Barrier
Snapshotting (ABS), a lightweight algorithm suited for modern dataflow
execution engines that minimises space requirements. ABS persists only operator
states on acyclic execution topologies while keeping a minimal record log on
cyclic dataflows. We implemented ABS on Apache Flink, a distributed analytics
engine that supports stateful stream processing. Our evaluation shows that our
algorithm does not have a heavy impact on the execution, maintaining linear
scalability and performing well with frequent snapshots.Comment: 8 pages, 7 figure
Geospatial Data Management Research: Progress and Future Directions
Without geospatial data management, todayÂŽs challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and âin-situâ geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objectsâ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis
Area theorem and smoothness of compact Cauchy horizons
We obtain an improved version of the area theorem for not necessarily
differentiable horizons which, in conjunction with a recent result on the
completeness of generators, allows us to prove that under the null energy
condition every compactly generated Cauchy horizon is smooth and compact. We
explore the consequences of this result for time machines, topology change,
black holes and cosmic censorship. For instance, it is shown that compact
Cauchy horizons cannot form in a non-empty spacetime which satisfies the stable
dominant energy condition wherever there is some source content.Comment: 44 pages. v2: added Sect. 2.4 on the propagation of singularities and
a second version of the area theorem (Theor. 14) which quantifies the area
increase due to the jump se
Critical Overview of Loops and Foams
This is a review of the present status of loop and spin foam approaches to
quantization of four-dimensional general relativity. It aims at raising various
issues which seem to challenge some of the methods and the results often taken
as granted in these domains. A particular emphasis is given to the issue of
diffeomorphism and local Lorentz symmetries at the quantum level and to the
discussion of new spin foam models. We also describe modifications of these two
approaches which may overcome their problems and speculate on other promising
research directions.Comment: 75 page
Locally Collapsed 3-Manifolds
We prove that a 3-dimensional compact Riemannian manifold which is locally
collapsed, with respect to a lower curvature bound, is a graph manifold. This
theorem was stated by Perelman and was used in his proof of the geometrization
conjecture.Comment: Final versio
Discrete-time gradient flows and law of large numbers in Alexandrov spaces
We develop the theory of discrete-time gradient flows for convex functions on
Alexandrov spaces with arbitrary upper or lower curvature bounds. We employ
different resolvent maps in the upper and lower curvature bound cases to
construct such a flow, and show its convergence to a minimizer of the potential
function. We also prove a stochastic version, a generalized law of large
numbers for convex function valued random variables, which not only extends
Sturm's law of large numbers on nonpositively curved spaces to arbitrary lower
or upper curvature bounds, but this version seems new even in the Euclidean
setting. These results generalize those in nonpositively curved spaces (partly
for squared distance functions) due to Ba\v{c}\'ak, Jost, Sturm and others, and
the lower curvature bound case seems entirely new.Comment: 28 pages; minor corrections; to appear in Calc. Var. PD
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