Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Jorge Arnulfo"'
A bipartite graph extensively models relationships between real-world entities of two different types, such as user-product data in e-commerce. Such graph data are inherently becoming more and more streaming, entailing continuous insertions and delet
Externí odkaz:
http://arxiv.org/abs/2312.03435
Graphs in many applications, such as social networks and IoT, are inherently streaming, involving continuous additions and deletions of vertices and edges at high rates. Constructing random walks in a graph, i.e., sequences of vertices selected with
Externí odkaz:
http://arxiv.org/abs/2209.06063
Autor:
Aksoy, Ahmet Kerem, Dushev, Pavel, Zacharatou, Eleni Tzirita, Hemsen, Holmer, Charfuelan, Marcela, Quiané-Ruiz, Jorge-Arnulfo, Demir, Begüm, Markl, Volker
The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes an
Externí odkaz:
http://arxiv.org/abs/2208.10830
A core operation in data discovery is to find joinable tables for a given table. Real-world tables include both unary and n-ary join keys. However, existing table discovery systems are optimized for unary joins and are ineffective and slow in the exi
Externí odkaz:
http://arxiv.org/abs/2110.00318
Data, algorithms, and compute/storage infrastructure are key assets that drive data science and artificial intelligence applications. As providing all these assets requires a huge investment, data science and artificial intelligence technologies are
Externí odkaz:
http://arxiv.org/abs/1909.03026
We propose AI-CARGO, a revenue management system for air-cargo that combines machine learning prediction with decision-making using mathematical optimization methods. AI-CARGO addresses a problem that is unique to the air-cargo business, namely the w
Externí odkaz:
http://arxiv.org/abs/1905.09130
Autor:
Chawla, Sanjay, Contreras-Rojas, Bertty, Kaoudi, Zoi, Kruse, Sebastian, Quiané-Ruiz, Jorge-Arnulfo
Today, organizations typically perform tedious and costly tasks to juggle their code and data across different data processing platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging because it requ
Externí odkaz:
http://arxiv.org/abs/1805.11723
Autor:
Kruse, Sebastian, Kaoudi, Zoi, Contreras, Bertty, Chawla, Sanjay, Naumann, Felix, Quiané-Ruiz, Jorge-Arnulfo
Publikováno v:
VLDB Journal 2020
In pursuit of efficient and scalable data analytics, the insight that "one size does not fit all" has given rise to a plethora of specialized data processing platforms and today's complex data analytics are moving beyond the limits of a single platfo
Externí odkaz:
http://arxiv.org/abs/1805.03533
Autor:
Kaoudi, Zoi, Quiané-Ruiz, Jorge-Arnulfo, Thirumuruganathan, Saravanan, Chawla, Sanjay, Agrawal, Divy
As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will
Externí odkaz:
http://arxiv.org/abs/1703.09193
Big data applications have fast arriving data that must be quickly ingested. At the same time, they have specific needs to preprocess and transform the data before it could be put to use. The current practice is to do these preparatory transformation
Externí odkaz:
http://arxiv.org/abs/1701.06093