Big data actionable intelligence architecture

Tian, Jinchuan; Garcia, Rudy J.; Danford, Forest; Patrizi, Laura; Galasso, Jennifer; Loyd, Jason

doi:10.1186/s40537-020-00378-7

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The amount of data created worldwide is ever-increasing, and the demand for high-density storage systems is growing 1 , 2 . Though current data storage technologies are constantly improving regarding speed, capacity, and robustness, they are already outpaced and cannot cope with the exponentially growing demand for storage.…”

Section: Introductionmentioning

confidence: 99%

Content-based filter queries on DNA data storage systems

El-Shaikh

Seeger

2023

Sci Rep

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Recent developments in DNA data storage systems have revealed the great potential to store large amounts of data at a very high density with extremely long persistence and low cost. However, despite recent contributions to robust data encoding, current DNA storage systems offer limited support for random access on DNA storage devices due to restrictive biochemical constraints. Moreover, state-of-the-art approaches do not support content-based filter queries on DNA storage. This paper introduces the first encoding for DNA that enables content-based searches on structured data like relational database tables. We provide the details of the methods for coding and decoding millions of directly accessible data objects on DNA. We evaluate the derived codes on real data sets and verify their robustness.

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Section: Introductionmentioning

confidence: 99%

Content-based filter queries on DNA data storage systems

El-Shaikh

Seeger

2023

Sci Rep

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“…If data are not transformed into immediate information, the generating of actionable intelligence will be delayed. This challenges the human capacity to store and review the data after the fact [ 2 ]. Detecting small objects in real time using remote sensors is an ongoing and challenging problem [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

Tian

2023

Sensors

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In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.

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“…Increasing the level of automation and intellectualization level of surrounding systems raises expectations about their usefulness and intelligence. Therefore, the development of Big Data processing systems with actionable abilities [ 1 ] and sensor technologies complemented by the Internet of Things (IoT) [ 2 ] brings significant improvements in Human–Machine Interfaces (HMI) [ 3 ]. Some of the most advanced human–machine interfaces can create surroundings [ 4 ] virtually indistinguishable from a real multimodal perceive-and-react human environment.…”

Section: Introductionmentioning

confidence: 99%

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

et al. 2021

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Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

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Big data actionable intelligence architecture

Cited by 8 publications

References 21 publications

Content-based filter queries on DNA data storage systems

Content-based filter queries on DNA data storage systems

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

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