Enhancing Big Data with Semantics: The AsterixDB Approach (Poster)

Alkowaileet, Wail Y.; Alsubaiee, Sattam; Carey, Michael J.; Li, Chen; Ramampiaro, Heri; Sinthong, Phanwadee; Wang, Xikui

doi:10.1109/icsc.2018.00063

Cited by 5 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The TweetFeed is connected to the dataset Tweets so that the ingested data can be persisted in storage (partitioned across all nodes of a cluster) directly for later use. There are two types of data feeds: static feeds, which maximize ingestion throughput, and dynamic feeds, which allow users to enrich incoming data using user-defined functions (UDFs) [21]- [24].…”

Section: A Data Feedsmentioning

confidence: 99%

Bridging BAD Islands: Declarative Data Sharing at Scale

Wang

Carey

Tsotras

2020

2020 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

In many Big Data applications today, information needs to be actively shared between systems managed by different organizations. To enable sharing Big Data at scale, developers would have to create dedicated server programs and glue together multiple Big Data systems for scalability. Developing and managing such glued data sharing services requires a significant amount of work from developers. In our prior work, we developed a Big Active Data (BAD) system for enabling Big Data subscriptions and analytics with millions of subscribers. Based on that, we introduce a new mechanism for enabling the sharing of Big Data at scale declaratively so that developers can easily create and provide data sharing services using declarative statements and can benefit from an underlying scalable infrastructure. We show our implementation on top of the BAD system, explain the data sharing data flow among multiple systems, and present a prototype system with experimental results.

show abstract

Section: A Data Feedsmentioning

confidence: 99%

Bridging BAD Islands: Declarative Data Sharing at Scale

Wang

Carey

Tsotras

2020

2020 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

show abstract

“…or infer, some aspects of a given process, crucial remains the process of harnessing those piles of data into realistic findings. No matter how much sophisticated is the algorithm that will analyze a dataset, equally critical is the statistical validity, the sense, the references, the subtle implications, in one simple word: the semantics, being inherent in those data [4][5][6].…”

mentioning

confidence: 99%

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

et al. 2019

View full text Add to dashboard Cite

If modern artificial intelligence (AI) comes often misunderstood, this is mainly due to the fact that, historically, it is solely tied to the way human brains work and think. New machine learning (ML) algorithms, instead, learn now by processing massive piles of data. This process enables machines to adapt to real-world situations, as well as to propose suggestions on how to classify and interpret a variety of different real phenomena. Simply speaking, the deployment of modern ML systems into critical applications is directly influenced by the way training data are organized and modeled [1-3]. Hence, while those modern algorithms rapidly sift through huge datasets, loaded with millions of information, a thoughtfully designed AI, beyond its ML-based core, should never disregard the fact that algorithms that learn are, for now, just another form of machine instruction, still guided and influenced by the potential and the limitations that training data carry with them. In other words, even when we train algorithms to learn basic associations that can then be used to approximate,

show abstract