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Modern applications commonly need to manage dataset types composed of heterogeneous data and schemas, making it difficult to access them in an integrated way. A single data store to manage heterogeneous data using a common data model is not…
Graph databases are becoming widely successful as data models that allow to effectively represent and process complex relationships among various types of data. As with any other type of data repository, graph databases may suffer from…
Microservices architectures have become the foundation for developing scalable and modern software systems, but they also bring significant challenges in managing heterogeneous and distributed data. The pragmatic solution is polyglot…
It is increasingly common to find complex data represented through the graph model. Contrary to relational models, graphs offer a high capacity for executing analytical tasks on complex data. Since a huge amount of data is still presented…
Most NoSQL systems are schema-on-read: data can be stored without first having to declare a Schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data frequently change.…
This proposal presents a graph computing framework intending to support both online and offline computing on large dynamic graphs efficiently. The framework proposes a new data model to support rich evolving vertex and edge data types. It…
We consider the problem of optimizing the interconnection graphs of complex networks to promote synchronization. When traditional optimization methods are inapplicable, due to uncertain or unknown node dynamics, we propose a data-driven…
Choosing the right Visualization techniques is critical in Big Data Analytics. However, decision makers are not experts on visualization and they face up with enormous difficulties in doing so. There are currently many different (i) Big…
Information, stored or transmitted in digital form, is often structured. Individual data records are usually represented as hierarchies of their elements. Together, records form larger structures. Information processing applications have to…
To succeed in a Big Data strategy, you have to arm yourself with a wide range of data skills and best practices. This strategy can result in an impressive asset that can streamline operational costs, reduce time to market, and enable the…
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acquisition, existing pipelines remain fragmented and…
Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications,…
Modern hardware architectures, e.g., NUMA servers, chiplet processors, tiered and disaggregated memory systems have significantly improved the performance of Main-Memory Databases, and are poised to deliver further improvements in the…
Since the use of computers in the business world, data collection has become one of the most important issues due to the available knowledge in the data; such data has been stored in the database. The database system was developed which led…
Deterministic databases enable scalable replicated systems by executing transactions in a predetermined order. However, existing designs fail to capture transaction dependencies, leading to insufficient scheduling, high abort rates, and…
The Entity-Relationship (ER) model is widely used for creating ER schemas for modeling application domains in the field of Information Systems development. However, when an ER schema is transformed to a Relational Database Schema (RDS),…
Nowadays, the explosion of unstructured data presents immense analytical value. Leveraging the remarkable capability of large language models (LLMs) in extracting attributes of structured tables from unstructured data, researchers are…
MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are…
Motion simulation, prediction and planning are foundational tasks in autonomous driving, each essential for modeling and reasoning about dynamic traffic scenarios. While often addressed in isolation due to their differing objectives, such…
In the real business world, data is stored in a variety of sources, including structured relational databases, unstructured databases (e.g., NoSQL databases), or even CSV/excel files. The ability to extract reasonable insights across these…