Top- Durable Graph Pattern Queries on Temporal Graphs

Semertzidis, Konstantinos; Pitoura, Evaggelia

doi:10.1109/tkde.2018.2823754

Cited by 33 publications

(15 citation statements)

References 29 publications

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“…Durable queries also arise in dynamic or temporal graphs, typically represented as sequences of graph snapshots. For example, in [31] and [32], authors considered the problem of finding the (top-k) most durable matches of an input graph pattern query; that is, the matches that exist for the longest period of time. The main focus is on the representations and indexes of the sequence of graph snapshots, and how to adapt classic graph algorithms in this setting.…”

Section: Related Workmentioning

confidence: 99%

Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

Gao

Sintos

Agarwal

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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A way of finding interesting or exceptional records from instant-stamped temporal data is to consider their "durability," or, intuitively speaking, how well they compare with other records that arrived earlier or later, and how long they retain their supremacy. For example, people are naturally fascinated by claims with long durability, such as: "On January 22, 2006, Kobe Bryant dropped 81 points against Toronto Raptors. Since then, this scoring record has yet to be broken." In general, given a sequence of instant-stamped records, suppose that we can rank them by a user-specified scoring function f , which may consider multiple attributes of a record to compute a single score for ranking. This paper studies durable top-k queries, which find records whose scores were within top-k among those records within a "durability window" of given length, e.g., a 10-year window starting/ending at the timestamp of the record. The parameter k, the length of the durability window, and parameters of the scoring function (which capture user preference) can all be given at the query time. We illustrate why this problem formulation yields more meaningful answers in some practical situations than other similar types of queries considered previously. We propose new algorithms for solving this problem, and provide a comprehensive theoretical analysis on the complexities of the problem itself and of our algorithms. Our algorithms vastly outperform various baselines (by up to two orders of magnitude on real and synthetic datasets).

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Section: Related Workmentioning

confidence: 99%

Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

Gao

Sintos

Agarwal

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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“…There has been limited work on querying and indexing over specific temporal features of property graph. [23] propose a model for finding the top-k graph patterns which exist for longest period of time over a series of graph snapshots. The propose several indexing techniques to minimize the snapshot search space, and perform a brute-force pattern mining on the restricted set.…”

Section: Property and Temporal Graph Queryingmentioning

confidence: 99%

A Distributed Path Query Engine for Temporal Property Graphs

Ramesh,

Baranawal,

Simmhan

2020

Preprint

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Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and properties and structure varying over time. These are seen in social, telecom and transit networks. However, current graph databases and query engines have limited support for temporal relations among graph entities, no support for time-varying entities and/or do not scale on distributed resources. We address this gap by extending a linear path query model over property graphs to include intuitive temporal predicates that operate over temporal graphs. We design a distributed execution model for these temporal path queries using the interval-centric computing model, and develop a novel cost model to select an efficient execution plan from several. We perform detailed experiments of our Granite distributed query engine using temporal property graphs as large as 52M vertices, 218M edges, and 118M properties, and a 800-query workload, derived from the LDBC benchmark. We offer sub-second query latencies in most cases, which is 154×-1786× faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph/Spark distributed graph query engine. Further, our cost model selects a query plan that is within 10% of the optimal execution time in 90% of the cases. We also scale well, and complete 100% of the query workload for all graphs, as opposed to only 32-90% completion by the baseline systems.

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“…In addition to the basic time-dependent graph pattern query problem, there are some more complex isomorphic problems exist in the time-dependent graph. Semertzidis and Pitoura [66] presented a durable graph pattern queries that aim to find persistent matches (the top-k longest period of time) of input patterns in node-labeled time-dependent graph. The evolution of graph is expressed as a sequence of snapshots which is corresponding to graphs with different time instances.…”

Section: Querying Time-dependent Graph Patternmentioning

confidence: 99%

Time-Dependent Graphs: Definitions, Applications, and Algorithms

Wang¹,

Yuan²,

Ma³

et al. 2019

Data Sci. Eng.

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A time-dependent graph is, informally speaking, a graph structure dynamically changes with time. In such graphs, the weights associated with edges dynamically change over time, that is, the edges in such graphs are activated by sequences of time-dependent elements. Many real-life scenarios can be better modeled by time-dependent graphs, such as bioinformatics networks, transportation networks, and social networks. In particular, the time-dependent graph is a very broad concept, which is reflected in the related research with many names, including temporal graphs, evolving graphs, time-varying graphs, historical graphs, and so on. Though static graphs have been extensively studied, for their time-dependent generalizations, we are still far from a complete and mature theory of models and algorithms. In this paper, we discuss the definition and topological structure of time-dependent graphs, as well as models for their relationship to dynamic systems. In addition, we review some classic problems on time-dependent graphs, e.g., route planning, social analysis, and subgraph problem (including matching and mining). We also introduce existing time-dependent systems and summarize their advantages and limitations. We try to keep the descriptions consistent as much as possible and we hope the survey can help practitioners to understand existing time-dependent techniques.

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Top- Durable Graph Pattern Queries on Temporal Graphs

Cited by 33 publications

References 29 publications

Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions

A Distributed Path Query Engine for Temporal Property Graphs

Time-Dependent Graphs: Definitions, Applications, and Algorithms

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