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1. (WO2018201948) USING MACHINE LEARNING TO ESTIMATE QUERY RESOURCE CONSUMPTION IN MPPDB
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Pub. No.: WO/2018/201948 International Application No.: PCT/CN2018/084464
Publication Date: 08.11.2018 International Filing Date: 25.04.2018
IPC:
G06F 17/30 (2006.01) ,G06F 9/445 (2018.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
17
Digital computing or data processing equipment or methods, specially adapted for specific functions
30
Information retrieval; Database structures therefor
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
9
Arrangements for programme control, e.g. control unit
06
using stored programme, i.e. using internal store of processing equipment to receive and retain programme
44
Arrangements for executing specific programmes
445
Programme loading or initiating
Applicants:
HUAWEI TECHNOLOGIES CO., LTD. [CN/CN]; Huawei Administration Building Bantian, Longgang District Shenzhen, Guangdong 518129, CN
Inventors:
LIU, Lei; US
ZHANG, Mingyi; US
DONG, Yu; US
LI, Huaizhi; US
QIAO, Yantao; US
Priority Data:
15/959,44223.04.2018US
62/492,70601.05.2017US
Title (EN) USING MACHINE LEARNING TO ESTIMATE QUERY RESOURCE CONSUMPTION IN MPPDB
(FR) UTILISATION D'APPRENTISSAGE AUTOMATIQUE POUR ESTIMER LA CONSOMMATION DE RESSOURCES D'INTERROGATION DANS UNE MPPDB
Abstract:
(EN) Methods and apparatus are provided for using machine learning to estimate query resource consumption in a massively parallel processing database (MPPDB). In various embodiments, the machine learning may jointly perform query resource consumption estimation for a query and resource extreme events detection together, utilize an adaptive kernel that is configured to learn most optimal similarity relation metric for data from each system settings, and utilize multi-level stacking technology configured to leverage outputs of diverse base classifier models. Advantages and benefits of the disclosed embodiments include providing faster and more reliable system performance and avoiding resource issues such as out of memory (OOM) occurrences.
(FR) L'invention concerne des procédés et un appareil permettant d'utiliser un apprentissage automatique pour estimer la consommation de ressources d'interrogation dans une base de données à traitement massivement parallèle (MPPDB). Dans divers modes de réalisation, l'apprentissage automatique peut effectuer conjointement une estimation de consommation de ressources d'interrogation pour une interrogation et une détection d'événements extrêmes pour les ressources, utiliser un noyau adaptatif qui est configuré pour apprendre la métrique de relation de similarité la plus optimale pour des données à partir de chaque configuration de système, et utiliser une technologie d'empilement à niveaux multiples configurée pour tirer parti de sorties de divers modèles classificateurs de base. Les avantages des modes de réalisation de l'invention sont, entre autres, la fourniture d'une performance de système plus rapide et plus fiable et l'évitement de problèmes de ressources tels que des occurrences de mémoire insuffisante (OOM).
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Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: English (EN)
Filing Language: English (EN)