Achieving online big data processing: from offline to online data analytics
Notice bibliographique
Résumé
Nowadays, a huge volume of data is increasingly produced by various sources, such as mobile sensing devices, system logs, and user activities over Internet.Accordingly, the information hidden in the massive size of data has a great potential to change and benefit our lives in many fields.To explore the values from data, researchers have made efforts to provide solutions to accommodate the big data applications.These applications not only require scalability on computation and storage but also bring a pressing challenge on data processing speed.If the data processing speed cannot catch up with the data generation speed, the computing results provided by the data analytic systems will be useless.Additionally, in many applications such as real-time recommendation system and web index maintenance, dataset involved in the computation needs to be frequently updated.To catch the change of individual data items and deliver the correct result reactively under the fast evolving dataset, we have to adopt online data analytic systems.Unfortunately, there exists a gap between the state-of-the-art data analytic system and the requirement of the online data analytics.It prevents the current data analytic systems/algorithms from being adopted in the more challenging, yet more realistic, online scenarios.In this thesis, we identify three major requirements to move the conventional offline computing systems to online: (1) First, we need to build a fluent online processing pipeline which consumes input data reactively and with high throughput; (2) We desire a storage layer which serves online query and update to the computing state so that it can deliver the results adaptively against the fast evolving dataset; (3) Finally, while moving from offline to online, the data analytic systems should be backward compatible to its offline counterparts, hence, we need to keep the maximum compatibility with infrastructure, programming model, etc.ii In this thesis, we study the solutions to fulfill these three requirements.(1) We design and implement the micro-batch-based online data processing pipeline with fine-grained resource allocation to avoid the per-record backup and handle the highly-skewed computing demands, e.g.videos with various length.(2) We build a parallel-friendly and high-performance computing state management system based on Locality Sensitive Hashing (LSH).Our system differentiates with the conventional LSH system in that it does not need to reconstruct itself to serve the online update requests and adopt multiple system-oriented optimization strategies to scale to large storage size and the multi-cores environment.(3) We achieve the scalable, efficient and reliable computing state management for online data analytics by introducing Resilient State Table (RST) as a component of Spark.So the system we have designed can seamlessly integrate with the programming model and scheduling policy of the original Spark framework.We evaluate our design in various scenarios.Our design exceeds the performance of the state-of-the-art systems in many application domains, e.g. the content-based video indexing, high dimensional nearest neighbors search, real-time web index maintenance, and machine learning algorithms.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,010 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».