FluteDB: An efficient and scalable in-memory time series database for sensor-cloud
Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availabili...
Uložené v:
| Vydané v: | Journal of parallel and distributed computing Ročník 122; s. 95 - 108 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Inc
01.12.2018
|
| Predmet: | |
| ISSN: | 0743-7315, 1096-0848 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availability are also difficult to be guaranteed. To deal with the problem, this paper proposes FluteDB, an efficient and scalable in-memory time series database for sensor-cloud. We adequately analyze the unique characteristics of time series data and its relevant operations to strike the right balance among efficiency, scalability, resources consumption, reliability and availability. Specifically, on basis of the aggregate analysis of root cause for ongoing time series problems, FluteDB targeted optimizes the strategies for key operations in memory and physical storage, at the expense of partial acceptable data precision and consistency. FluteDB’s enhanced strategies are primarily comprised of Triggered Time Series Merge Tree (TTSM Tree), time series enhanced cache management and corresponding compression algorithms for different data types. The validations of all sub-modules have demonstrated that our improved strategies outperform existing methods in real time series environment significantly. Global experimental results also show that the integrated FluteDB reduces query latency by 17x, improves write rate by 98x and saves about 47% storage resources. The average available service time and recovery rate and degree of FluteDB are competitive with the state-of-the-art reliability and availability strategy in real and simulated faults, which demonstrates FluteDB can provide highly stable large-scale data cloud services.
•FluteDB is an efficient and scalable time series database for sensor-cloud.•The index in FluteDB equips flexible storage tricks for time series data.•FluteDB improves efficiency by adjusting disk accesses according to data temperature.•FluteDB optimizes its data encapsulation and fault tolerant strategies. |
|---|---|
| AbstractList | Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availability are also difficult to be guaranteed. To deal with the problem, this paper proposes FluteDB, an efficient and scalable in-memory time series database for sensor-cloud. We adequately analyze the unique characteristics of time series data and its relevant operations to strike the right balance among efficiency, scalability, resources consumption, reliability and availability. Specifically, on basis of the aggregate analysis of root cause for ongoing time series problems, FluteDB targeted optimizes the strategies for key operations in memory and physical storage, at the expense of partial acceptable data precision and consistency. FluteDB’s enhanced strategies are primarily comprised of Triggered Time Series Merge Tree (TTSM Tree), time series enhanced cache management and corresponding compression algorithms for different data types. The validations of all sub-modules have demonstrated that our improved strategies outperform existing methods in real time series environment significantly. Global experimental results also show that the integrated FluteDB reduces query latency by 17x, improves write rate by 98x and saves about 47% storage resources. The average available service time and recovery rate and degree of FluteDB are competitive with the state-of-the-art reliability and availability strategy in real and simulated faults, which demonstrates FluteDB can provide highly stable large-scale data cloud services.
•FluteDB is an efficient and scalable time series database for sensor-cloud.•The index in FluteDB equips flexible storage tricks for time series data.•FluteDB improves efficiency by adjusting disk accesses according to data temperature.•FluteDB optimizes its data encapsulation and fault tolerant strategies. |
| Author | Li, Chen Li, Jianxin Li, Bo Wei, Guanyu Wang, Lihong Si, Jinghui Bhuiyan, Md Zakirul Alam |
| Author_xml | – sequence: 1 givenname: Chen surname: Li fullname: Li, Chen email: lichen@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 2 givenname: Bo surname: Li fullname: Li, Bo email: libo@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 3 givenname: Md Zakirul Alam surname: Bhuiyan fullname: Bhuiyan, Md Zakirul Alam email: mbhuiyan3@fordham.edu organization: Department of Computer and Information Sciences, Fordham University, Bronx, NY, USA – sequence: 4 givenname: Lihong surname: Wang fullname: Wang, Lihong email: wlh@isc.org.cn organization: National Internet Emergency Center, Beijing, China – sequence: 5 givenname: Jinghui surname: Si fullname: Si, Jinghui email: sijh@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 6 givenname: Guanyu surname: Wei fullname: Wei, Guanyu email: weigy@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 7 givenname: Jianxin surname: Li fullname: Li, Jianxin email: lijx@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China |
| BookMark | eNp90M1qwzAMwHEzOljb7QV28gskk-06SccuXfcJhcHYzkaxFXBIk2K7g779UrrTDj0JBH-BfjM26YeeGLsVkAsQxV2btztncwmiyqHMQYoLNhWwLDKoFtWETaFcqKxUQl-xWYwtgBC6rKbs86XbJ3p6vOernlPTeOupTxx7x6PFDuuOuO-zLW2HcODJb4lHCp4id5iwxki8GcK46-MQMtsNe3fNLhvsIt38zTn7fnn-Wr9lm4_X9_Vqk1kFkLJaS1GAayRhU6OUugRUVsJS4cJJDZWVQpMSCotF7VxRlU1tl9qi01pWCtWcydNdG4YYAzVmF_wWw8EIMEcV05qjijmqGCjNqDJG1b_I-oTJD30K6Lvz6cMppfGpH0_BxCOWJecD2WTc4M_lvxjVf_k |
| CitedBy_id | crossref_primary_10_1016_j_aei_2023_102224 crossref_primary_10_1016_j_ins_2019_08_064 crossref_primary_10_3390_math9172146 crossref_primary_10_1016_j_bdr_2021_100206 crossref_primary_10_4018_JDM_339915 crossref_primary_10_1016_j_bdr_2021_100256 |
| Cites_doi | 10.1109/TPDS.2016.2552174 10.1145/2783258.2783348 10.1007/s00778-005-0171-7 10.1145/146941.146943 10.1109/TNET.2002.803864 10.1007/s11047-015-9536-z 10.1007/s002360050048 10.1109/TR.2013.2270415 10.1145/3035918.3056102 10.1145/954339.954341 10.1145/2213836.2213862 10.1007/978-3-319-72395-2_41 10.1145/568518.568520 10.1109/TIT.1977.1055714 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Inc. |
| Copyright_xml | – notice: 2018 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.jpdc.2018.07.021 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1096-0848 |
| EndPage | 108 |
| ExternalDocumentID | 10_1016_j_jpdc_2018_07_021 S0743731518305422 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 1~5 29L 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFSI ABJNI ABMAC ABTAH ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADFGL ADHUB ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CAG COF CS3 DM4 DU5 E.L EBS EFBJH EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA K-O KOM LG5 LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TN5 TWZ WUQ XJT XOL XPP ZMT ZU3 ZY4 ~G- ~G0 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-b52160df2eafba22570a3c2093a4d2508c215e313a64bdd687fbc95cad55283a3 |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000448232400008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0743-7315 |
| IngestDate | Sat Nov 29 07:17:08 EST 2025 Tue Nov 18 21:51:50 EST 2025 Fri Feb 23 02:31:21 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Disaster tolerant Sensor-cloud Scalability Time series In-memory database |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-b52160df2eafba22570a3c2093a4d2508c215e313a64bdd687fbc95cad55283a3 |
| PageCount | 14 |
| ParticipantIDs | crossref_primary_10_1016_j_jpdc_2018_07_021 crossref_citationtrail_10_1016_j_jpdc_2018_07_021 elsevier_sciencedirect_doi_10_1016_j_jpdc_2018_07_021 |
| PublicationCentury | 2000 |
| PublicationDate | December 2018 2018-12-00 |
| PublicationDateYYYYMMDD | 2018-12-01 |
| PublicationDate_xml | – month: 12 year: 2018 text: December 2018 |
| PublicationDecade | 2010 |
| PublicationTitle | Journal of parallel and distributed computing |
| PublicationYear | 2018 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Tuomas, Scott, Cavallaro, Huang, Justin, Justin, Kaushik (b22) 2015; 8 L. Chen, L. jianxin, S. Jinghui, Z. Yangyang, FluteDB: An Efficient and Dependable Time-Series Database Storage Engine, SpaCCS Workshops, 2017, pp. 446–456. Stavros, Kushal, Samuel, M. Timothy (b16) 2016; 10 Mario, Hugo J., Fernando, Eric S. (b6) 2017; 16 New York City Taxi Trip Duration, 2017 (Accessed 17 2017). R. Sean, W. Eric, W. Edmund, A. Ethan, S. Nat, Littletable A time-series database and its uses, in: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD, 2017, pp. 125–138. Michael (b8) 2002; 10 C. Yongjie, T. Hanghang, F. Wei, J. Ping, H. Qing, Facets: Fast comprehensive mining of coevolving high-order time series, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 79–88. TimescaleDB: SQL made scalable for time-series data, 2017. Stefan, László (b17) 2003; 35 OpenTSDB - A Distributed, Scalable Monitoring System, 2017 Robson E.D., Azzedine, Raed (b13) 2017; 28 Kaushik, Eamonn J., Sharad, Michael (b5) 2002; 27 The world’s most popular open source database, 2017. Mendel, John K. (b7) 1992; 10 The world’s most advanced open source database, 2017 (Accessed 17 December 2017). S. Russell, R. Raghu, bLSM: a general purpose log structured merge tree, in: Proceedings of the 2012 ACM International Conference on Management of Data, SIGMOD 2012, pp. 217–228. Patrick E., Edward, Dieter, Elizabeth J. (b12) 1996; 33 Mostafa A. (b9) 1985; 11 Storage Engine of InfluxData, 2017. Jacob, Abraham (b4) 1977; 23 Christopher M., Edward, Wai Gen (b2) 2007; 16 Gregory, Liudong, Hanoch, Yuanshun (b3) 2013; 62 Mendel (10.1016/j.jpdc.2018.07.021_b7) 1992; 10 Robson E.D. (10.1016/j.jpdc.2018.07.021_b13) 2017; 28 Jacob (10.1016/j.jpdc.2018.07.021_b4) 1977; 23 Michael (10.1016/j.jpdc.2018.07.021_b8) 2002; 10 10.1016/j.jpdc.2018.07.021_b19 Kaushik (10.1016/j.jpdc.2018.07.021_b5) 2002; 27 10.1016/j.jpdc.2018.07.021_b18 Christopher M. (10.1016/j.jpdc.2018.07.021_b2) 2007; 16 Mostafa A. (10.1016/j.jpdc.2018.07.021_b9) 1985; 11 Patrick E. (10.1016/j.jpdc.2018.07.021_b12) 1996; 33 10.1016/j.jpdc.2018.07.021_b20 Mario (10.1016/j.jpdc.2018.07.021_b6) 2017; 16 10.1016/j.jpdc.2018.07.021_b11 Stavros (10.1016/j.jpdc.2018.07.021_b16) 2016; 10 Gregory (10.1016/j.jpdc.2018.07.021_b3) 2013; 62 10.1016/j.jpdc.2018.07.021_b10 10.1016/j.jpdc.2018.07.021_b21 Tuomas (10.1016/j.jpdc.2018.07.021_b22) 2015; 8 10.1016/j.jpdc.2018.07.021_b23 10.1016/j.jpdc.2018.07.021_b1 10.1016/j.jpdc.2018.07.021_b15 Stefan (10.1016/j.jpdc.2018.07.021_b17) 2003; 35 10.1016/j.jpdc.2018.07.021_b14 |
| References_xml | – volume: 23 start-page: 337 year: 1977 end-page: 343 ident: b4 article-title: A universal algorithm for sequential data compression publication-title: IEEE Trans. Inform. Theory – volume: 10 start-page: 349 year: 2016 end-page: 360 ident: b16 article-title: The tiledb array data storage manager publication-title: Publ. Very Large Database Endowment (PVLDB) – reference: Storage Engine of InfluxData, 2017. – reference: TimescaleDB: SQL made scalable for time-series data, 2017. – reference: C. Yongjie, T. Hanghang, F. Wei, J. Ping, H. Qing, Facets: Fast comprehensive mining of coevolving high-order time series, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 79–88. – volume: 10 start-page: 26 year: 1992 end-page: 52 ident: b7 article-title: The design and implementation of a log-structured file system publication-title: ACM Trans. Comput. Syst. – reference: . (Accessed 17 2017). – reference: . (Accessed 17 December 2017). – volume: 28 start-page: 215 year: 2017 end-page: 229 ident: b13 article-title: Time series-oriented load prediction model and migration policies for distributed simulation systems publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 11 start-page: 1047 year: 1985 end-page: 1058 ident: b9 article-title: Data compression in scientific and statistical databases publication-title: IEEE Trans. Softw. Eng. – reference: R. Sean, W. Eric, W. Edmund, A. Ethan, S. Nat, Littletable A time-series database and its uses, in: Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD, 2017, pp. 125–138. – reference: The world’s most popular open source database, 2017. – reference: New York City Taxi Trip Duration, 2017, – reference: S. Russell, R. Raghu, bLSM: a general purpose log structured merge tree, in: Proceedings of the 2012 ACM International Conference on Management of Data, SIGMOD 2012, pp. 217–228. – volume: 10 start-page: 604 year: 2002 end-page: 612 ident: b8 article-title: Compressed bloom filters publication-title: IEEE/ACM Trans. Netw. – volume: 35 start-page: 374 year: 2003 end-page: 398 ident: b17 article-title: A survey of web cache replacement strategies publication-title: ACM Comput. Surv. – reference: OpenTSDB - A Distributed, Scalable Monitoring System, 2017, – volume: 16 start-page: 165 year: 2017 end-page: 174 ident: b6 article-title: Time series forecasting with genetic programming publication-title: Nat. Comput. – reference: The world’s most advanced open source database, 2017, – volume: 27 start-page: 188 year: 2002 end-page: 228 ident: b5 article-title: Locally adaptive dimensionality reduction for indexing large time series databases publication-title: ACM Trans. Database Syst. – volume: 62 start-page: 637 year: 2013 end-page: 647 ident: b3 article-title: Reliability of series-parallel systems with random failure propagation time publication-title: IEEE Trans. Reliab. – volume: 33 start-page: 351 year: 1996 end-page: 385 ident: b12 article-title: The log-structured merge-tree (LSM-Tree) publication-title: Acta Inf. – volume: 16 start-page: 417 year: 2007 end-page: 437 ident: b2 article-title: The partitioned exponential file for database storage management publication-title: VLDB J. – volume: 8 start-page: 1816 year: 2015 end-page: 1827 ident: b22 article-title: Gorilla: a fast, scalable, in-memory time series database publication-title: Publ. Very Large Database Endowment (PVLDB) – reference: L. Chen, L. jianxin, S. Jinghui, Z. Yangyang, FluteDB: An Efficient and Dependable Time-Series Database Storage Engine, SpaCCS Workshops, 2017, pp. 446–456. – volume: 28 start-page: 215 issue: 1 year: 2017 ident: 10.1016/j.jpdc.2018.07.021_b13 article-title: Time series-oriented load prediction model and migration policies for distributed simulation systems publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2016.2552174 – ident: 10.1016/j.jpdc.2018.07.021_b23 doi: 10.1145/2783258.2783348 – volume: 16 start-page: 417 issue: 4 year: 2007 ident: 10.1016/j.jpdc.2018.07.021_b2 article-title: The partitioned exponential file for database storage management publication-title: VLDB J. doi: 10.1007/s00778-005-0171-7 – volume: 10 start-page: 26 issue: 1 year: 1992 ident: 10.1016/j.jpdc.2018.07.021_b7 article-title: The design and implementation of a log-structured file system publication-title: ACM Trans. Comput. Syst. doi: 10.1145/146941.146943 – ident: 10.1016/j.jpdc.2018.07.021_b19 – ident: 10.1016/j.jpdc.2018.07.021_b18 – volume: 10 start-page: 604 issue: 5 year: 2002 ident: 10.1016/j.jpdc.2018.07.021_b8 article-title: Compressed bloom filters publication-title: IEEE/ACM Trans. Netw. doi: 10.1109/TNET.2002.803864 – volume: 16 start-page: 165 issue: 1 year: 2017 ident: 10.1016/j.jpdc.2018.07.021_b6 article-title: Time series forecasting with genetic programming publication-title: Nat. Comput. doi: 10.1007/s11047-015-9536-z – volume: 8 start-page: 1816 issue: 12 year: 2015 ident: 10.1016/j.jpdc.2018.07.021_b22 article-title: Gorilla: a fast, scalable, in-memory time series database publication-title: Publ. Very Large Database Endowment (PVLDB) – volume: 33 start-page: 351 issue: 4 year: 1996 ident: 10.1016/j.jpdc.2018.07.021_b12 article-title: The log-structured merge-tree (LSM-Tree) publication-title: Acta Inf. doi: 10.1007/s002360050048 – volume: 62 start-page: 637 issue: 3 year: 2013 ident: 10.1016/j.jpdc.2018.07.021_b3 article-title: Reliability of series-parallel systems with random failure propagation time publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2013.2270415 – ident: 10.1016/j.jpdc.2018.07.021_b15 doi: 10.1145/3035918.3056102 – ident: 10.1016/j.jpdc.2018.07.021_b21 – volume: 10 start-page: 349 issue: 4 year: 2016 ident: 10.1016/j.jpdc.2018.07.021_b16 article-title: The tiledb array data storage manager publication-title: Publ. Very Large Database Endowment (PVLDB) – volume: 35 start-page: 374 issue: 4 year: 2003 ident: 10.1016/j.jpdc.2018.07.021_b17 article-title: A survey of web cache replacement strategies publication-title: ACM Comput. Surv. doi: 10.1145/954339.954341 – ident: 10.1016/j.jpdc.2018.07.021_b20 – ident: 10.1016/j.jpdc.2018.07.021_b14 doi: 10.1145/2213836.2213862 – ident: 10.1016/j.jpdc.2018.07.021_b1 doi: 10.1007/978-3-319-72395-2_41 – volume: 27 start-page: 188 issue: 2 year: 2002 ident: 10.1016/j.jpdc.2018.07.021_b5 article-title: Locally adaptive dimensionality reduction for indexing large time series databases publication-title: ACM Trans. Database Syst. doi: 10.1145/568518.568520 – ident: 10.1016/j.jpdc.2018.07.021_b10 – ident: 10.1016/j.jpdc.2018.07.021_b11 – volume: 11 start-page: 1047 issue: 10 year: 1985 ident: 10.1016/j.jpdc.2018.07.021_b9 article-title: Data compression in scientific and statistical databases publication-title: IEEE Trans. Softw. Eng. – volume: 23 start-page: 337 issue: 3 year: 1977 ident: 10.1016/j.jpdc.2018.07.021_b4 article-title: A universal algorithm for sequential data compression publication-title: IEEE Trans. Inform. Theory doi: 10.1109/TIT.1977.1055714 |
| SSID | ssj0011578 |
| Score | 2.2412007 |
| Snippet | Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 95 |
| SubjectTerms | Disaster tolerant In-memory database Scalability Sensor-cloud Time series |
| Title | FluteDB: An efficient and scalable in-memory time series database for sensor-cloud |
| URI | https://dx.doi.org/10.1016/j.jpdc.2018.07.021 |
| Volume | 122 |
| WOSCitedRecordID | wos000448232400008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1096-0848 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011578 issn: 0743-7315 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZWLQcuvBHlJR-4rYKc19rhtoVWgEqFUJFWXCLHdtSs0mS1u6naH8F_Zia2s9sCFT1wsSLLmTzm03gymfmGkDdFyWKTaRkwIQV8oBgRCMNYwIsoVFk_9iSuR_z4WMxm2dfR6KevhTmvedOIi4ts8V9VDXOgbCydvYW6B6EwAcegdBhB7TD-k-IPQbj5sO8ifqaniPCJ5CvQSF8rVTXBGebYXvbN5cd4Y2Y1xnRR3Nb63MMVfOC2y0DVbXelmeeWC4u84XVtLNuARgpe7J5l-kK5Rbf2uyLm-1T23_6m8MzO7LdDPOC0qy5tNPaLHv8Ax3bZAXpqebaJ-Vu7dFSdtk6yC1eEYiv1w1o15ETlsa3hHEywrU12RtR23XTbcdjTPvxu6W3QYf52vtDIRBmKnoPVVltfpdW-tt0NSYg-v22eo4wcZeSM5wxpCXYjnmZgJHennw5mn4ffUmFqt3b_DK4KyyYMXr-TP3s6W97LyQNyz-mMTi1cHpKRaR6R-76lB3UW_jH55tDzjk4bOmCHgn6pxw4dsEMRO9Rih3rsUMAO3cbOE_L98ODk_cfAtd0IVMzYOijAo5swXUZGloWMsM2hjFXEslgmGjxmocBNNHEYy0lSaD0RvCxUliqpU2QKkvFTstO0jXlGaKaKskhlzDkvk8RwWSah4LgS1mlT7pHQv6JcOU56bI1S539Xzh4ZD-csLCPLjatT_-Zz51NaXzEHIN1w3vNbXeUFubvB-kuys1525hW5o87X1Wr52qHoF1UXmIA |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=FluteDB%3A+An+efficient+and+scalable+in-memory+time+series+database+for+sensor-cloud&rft.jtitle=Journal+of+parallel+and+distributed+computing&rft.au=Li%2C+Chen&rft.au=Li%2C+Bo&rft.au=Bhuiyan%2C+Md+Zakirul+Alam&rft.au=Wang%2C+Lihong&rft.date=2018-12-01&rft.issn=0743-7315&rft.volume=122&rft.spage=95&rft.epage=108&rft_id=info:doi/10.1016%2Fj.jpdc.2018.07.021&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jpdc_2018_07_021 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0743-7315&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0743-7315&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0743-7315&client=summon |