There has been a growing interest in the graph-streaming setting where
a continuous stream of graph updates is mixed with graph queries. In
principle, purely-functional trees are an ideal fit for this
setting as they enable safe parallelism, lightweight snapshots, and
strict serializability for queries. However, directly using them for
graph processing leads to significant space overhead and poor cache
locality.
This paper presents C-trees, a compressed purely-functional
search tree data structure that significantly improves on the space
usage and locality of purely-functional trees. We design
theoretically-efficient and practical algorithms for performing batch
updates to C-trees, and also show that we can store massive
dynamic real-world graphs using only a few bytes per edge, thereby
achieving space usage close to that of the best static graph
processing frameworks.
To study the applicability of our data structure, we designed Aspen, a
graph-streaming framework that extends the interface of Ligra with
operations for updating graphs. We show that Aspen is faster than two
state-of-the-art graph-streaming systems, Stinger and LLAMA, while
requiring less memory, and is competitive in performance with the
state-of-the-art static graph frameworks, Galois, GAP, and Ligra+.
With Aspen, we are able to efficiently process the largest
publicly-available graph with over two hundred billion edges in the
graph-streaming setting using a single commodity multicore server with
1TB of memory.
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Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
Advances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory-efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.
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- Award ID(s):
- 1730396
- NSF-PAR ID:
- 10062986
- Date Published:
- Journal Name:
- Computer graphics forum
- ISSN:
- 1467-8659
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/cgf.13398
