287 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Mining Butterflies in Streaming Graphs
This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection.
sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Hypergraph Motifs and Their Extensions Beyond Binary
Hypergraphs naturally represent group interactions, which are omnipresent in
many domains: collaborations of researchers, co-purchases of items, and joint
interactions of proteins, to name a few. In this work, we propose tools for
answering the following questions: (Q1) what are the structural design
principles of real-world hypergraphs? (Q2) how can we compare local structures
of hypergraphs of different sizes? (Q3) how can we identify domains from which
hypergraphs are? We first define hypergraph motifs (h-motifs), which describe
the overlapping patterns of three connected hyperedges. Then, we define the
significance of each h-motif in a hypergraph as its occurrences relative to
those in properly randomized hypergraphs. Lastly, we define the characteristic
profile (CP) as the vector of the normalized significance of every h-motif.
Regarding Q1, we find that h-motifs' occurrences in 11 real-world hypergraphs
from 5 domains are clearly distinguished from those of randomized hypergraphs.
Then, we demonstrate that CPs capture local structural patterns unique to each
domain, and thus comparing CPs of hypergraphs addresses Q2 and Q3. The concept
of CP is extended to represent the connectivity pattern of each node or
hyperedge as a vector, which proves useful in node classification and hyperedge
prediction. Our algorithmic contribution is to propose MoCHy, a family of
parallel algorithms for counting h-motifs' occurrences in a hypergraph. We
theoretically analyze their speed and accuracy and show empirically that the
advanced approximate version MoCHy-A+ is more accurate and faster than the
basic approximate and exact versions, respectively. Furthermore, we explore
ternary hypergraph motifs that extends h-motifs by taking into account not only
the presence but also the cardinality of intersections among hyperedges. This
extension proves beneficial for all previously mentioned applications.Comment: Extended version of VLDB 2020 paper arXiv:2003.0185
Gabriel Vacariu (c2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy
Unbelievable similar ideas to my ideas published long before..
Differential Privacy for Clustering Under Continual Observation
We consider the problem of clustering privately a dataset in
that undergoes both insertion and deletion of points. Specifically, we give an
-differentially private clustering mechanism for the -means
objective under continual observation. This is the first approximation
algorithm for that problem with an additive error that depends only
logarithmically in the number of updates. The multiplicative error is
almost the same as non privately. To do so we show how to perform dimension
reduction under continual observation and combine it with a differentially
private greedy approximation algorithm for -means. We also partially extend
our results to the -median problem
Learning-Augmented B-Trees
We study learning-augmented binary search trees (BSTs) and B-Trees via Treaps
with composite priorities. The result is a simple search tree where the depth
of each item is determined by its predicted weight . To achieve the
result, each item has its composite priority
where is the uniform
random variable. This generalizes the recent learning-augmented BSTs
[Lin-Luo-Woodruff ICML`22], which only work for Zipfian distributions, to
arbitrary inputs and predictions. It also gives the first B-Tree data structure
that can provably take advantage of localities in the access sequence via
online self-reorganization. The data structure is robust to prediction errors
and handles insertions, deletions, as well as prediction updates.Comment: 25 page
Harnessing the Power of Distributed Computing: Advancements in Scientific Applications, Homomorphic Encryption, and Federated Learning Security
Data explosion poses lot of challenges to the state-of-the art systems, applications, and methodologies. It has been reported that 181 zettabytes of data are expected to be generated in 2025 which is over 150\% increase compared to the data that is expected to be generated in 2023. However, while system manufacturers are consistently developing devices with larger storage spaces and providing alternative storage capacities in the cloud at affordable rates, another key challenge experienced is how to effectively process the fraction of large scale of stored data in time-critical conventional systems. One transformative paradigm revolutionizing the processing and management of these large data is distributed computing whose application requires deep understanding. This dissertation focuses on exploring the potential impact of applying efficient distributed computing concepts to long existing challenges or issues in (i) a widely data-intensive scientific application (ii) applying homomorphic encryption to data intensive workloads found in outsourced databases and (iii) security of tokenized incentive mechanism for Federated learning (FL) systems.The first part of the dissertation tackles the Microelectrode arrays (MEAs) parameterization problem from an orthogonal viewpoint enlightened by algebraic topology, which allows us to algebraically parametrize MEAs whose structure and intrinsic parallelism are hard to identify otherwise. We implement a new paradigm, namely Parma, to demonstrate the effectiveness of the proposed approach and report how it outperforms the state-of-the-practice in time, scalability, and memory usage.The second part discusses our work on introducing the concept of parallel caching of secure aggregation to mitigate the performance overhead incurred by the HE module in outsourced databases. The key idea of this optimization approach is caching selected radix-ciphertexts in parallel without violating existing security guarantees of the primitive/base HE scheme. A new radix HE algorithm was designed and applied to both batch and incremental HE schemes, and experiments carried out on six workloads show that the proposed caching boost state-of-the-art HE schemes by high orders of magnitudes.In the third part, I will discuss our work on leveraging the security benefit of blockchains to enhance or protect the fairness and reliability of tokenized incentive mechanism for FL systems. We designed a blockchain-based auditing protocol to mitigate Gaussian attacks and carried out experiments with multiple FL aggregation algorithms, popular data sets and a variety of scales to validate its effectiveness
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
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