5,151 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Recommended from our members
Big data analytics for time critical maritime and aerial mobility forecasting
The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics
Spatial Data Quality in the IoT Era:Management and Exploitation
Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications
A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets
Estimating set similarity and detecting highly similar sets are fundamental
problems in areas such as databases, machine learning, and information
retrieval. MinHash is a well-known technique for approximating Jaccard
similarity of sets and has been successfully used for many applications such as
similarity search and large scale learning. Its two compressed versions, b-bit
MinHash and Odd Sketch, can significantly reduce the memory usage of the
original MinHash method, especially for estimating high similarities (i.e.,
similarities around 1). Although MinHash can be applied to static sets as well
as streaming sets, of which elements are given in a streaming fashion and
cardinality is unknown or even infinite, unfortunately, b-bit MinHash and Odd
Sketch fail to deal with streaming data. To solve this problem, we design a
memory efficient sketch method, MaxLogHash, to accurately estimate Jaccard
similarities in streaming sets. Compared to MinHash, our method uses smaller
sized registers (each register consists of less than 7 bits) to build a compact
sketch for each set. We also provide a simple yet accurate estimator for
inferring Jaccard similarity from MaxLogHash sketches. In addition, we derive
formulas for bounding the estimation error and determine the smallest necessary
memory usage (i.e., the number of registers used for a MaxLogHash sketch) for
the desired accuracy. We conduct experiments on a variety of datasets, and
experimental results show that our method MaxLogHash is about 5 times more
memory efficient than MinHash with the same accuracy and computational cost for
estimating high similarities
- …