TY - GEN
T1 - PIGEON
T2 - 2015 31st IEEE International Conference on Data Engineering, ICDE 2015
AU - Xie, Xiaojing
AU - Fan, Zhe
AU - Choi, Byron
AU - Yi, Peipei
AU - Bhowmick, Sourav S.
AU - Zhou, Shuigeng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/5/26
Y1 - 2015/5/26
N2 - Subgraph queries have been a fundamental query for retrieving patterns from graph data. Due to the well known NP hardness of subgraph queries, those queries may sometimes take a long time to complete. Our recent investigation on real- world datasets revealed that the performance of queries on graphs generally varies greatly. In other words, query clients may occasionally encounter 'unexpectedly' long execution from a subgraph query processor. This paper aims to demonstrate a tool that alleviates the problem by monitoring subgraph query progress. Specifically, we present a novel subgraph query progress indicator called PIGEON that exploits query-time information to report to users accurate estimated query progress. In the demonstration, users may interact with PIGEON to gain insights on the query evaluation, which include the following: Users are enabled to (i) monitor query progress; (ii) analyze the causes of long query times; and (iii) abort queries that run abnormally long, which may sometimes contain human errors.
AB - Subgraph queries have been a fundamental query for retrieving patterns from graph data. Due to the well known NP hardness of subgraph queries, those queries may sometimes take a long time to complete. Our recent investigation on real- world datasets revealed that the performance of queries on graphs generally varies greatly. In other words, query clients may occasionally encounter 'unexpectedly' long execution from a subgraph query processor. This paper aims to demonstrate a tool that alleviates the problem by monitoring subgraph query progress. Specifically, we present a novel subgraph query progress indicator called PIGEON that exploits query-time information to report to users accurate estimated query progress. In the demonstration, users may interact with PIGEON to gain insights on the query evaluation, which include the following: Users are enabled to (i) monitor query progress; (ii) analyze the causes of long query times; and (iii) abort queries that run abnormally long, which may sometimes contain human errors.
UR - http://www.scopus.com/inward/record.url?scp=84940875726&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2015.7113409
DO - 10.1109/ICDE.2015.7113409
M3 - Conference proceeding
AN - SCOPUS:84940875726
T3 - Proceedings - International Conference on Data Engineering
SP - 1492
EP - 1495
BT - 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PB - IEEE Computer Society
Y2 - 13 April 2015 through 17 April 2015
ER -