ANALOC: Efficient analytics over Location Based Services

Rahman, Farhadur; Suhaim, Saad Bin; Liu, Weimo; Thirumuruganathan, Saravanan; Zhang, Nan; Das, Gautam

doi:10.1109/icde.2016.7498346

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…And how algorithms like KNN can be used to preprocess and take decision on the LBS. [7] This paper gave insight of how to identify the Natural Disaster based on the previous data and also which can be used to process and predict current scenario. [8] This paper gave insight of how to build a framework for moving objects like Vehicles using service oriented architecture.…”

Section: Literature Surveymentioning

confidence: 99%

A Systematic Approach For Data Cleansing Process of Geospatial Data to Perform Application Specific Data Analytics

Satyanarayana¹,

M²,

P³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Data Analytics is the key word of today's era. Huge data is getting generated day by day from various resources starting from social networking sites to sensors then machines. How this can be handled in effective manner to get some value out of it, this is the biggest question in front of all engineers today. Geo Spatial Data, this data is another type of data which is getting produced because of the objects on the surface either they are static or dynamic. As per the statistics every year there is a 20% increase in Geospatial data production. And this Geospatial Data can be used for multiple purposes in various applications like autonomous vehicles, location based services, identifying the object in surface etc..., but the biggest challenge faced here is how this data can be analyzed and stored for future purpose. This data may be live data or stored data, it might be structured, un-structured or quasi structured data, it might be with duplicates or without duplicates and with null values or without null values. The challenge here is how this data can be used to perform data analytics and produce the results which can be used for future use. In the proposed research the main concentration is on how Geospatial data can be cleaned and made ready to use for data analytics for future use in applications like driverless vehicles, Location Based Services etc.., the first step in performing data analytics is collecting the Geospatial data then cleaning the same for further use. Once it is cleaned and ready to use the data analytics will be performed for further decision making.

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Section: Literature Surveymentioning

confidence: 99%

A Systematic Approach For Data Cleansing Process of Geospatial Data to Perform Application Specific Data Analytics

Satyanarayana¹,

M²,

P³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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“…In the present study, we apply HDBSCAN exclusively to MD trajectories. However, it has been tested in a variety of contexts of which we cite a few examples. ,− For example, HDBSCAN has been used to improve querying databases for location-based services and anomaly detection in signal processing . (For a more complete conceptual explanation and a toy example of HDBSCAN, see Methods.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

Melvin

Godwin

Xiao

et al. 2016

J. Chem. Theory Comput.

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As the length of molecular dynamics (MD) trajectories grows with increasing computational power, so does the importance of clustering methods for partitioning trajectories into conformational bins. Of the methods available, the vast majority require users to either have some a priori knowledge about the system to be clustered or to tune clustering parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of an MD trajectory. Being non-parametric, these methods require neither prior knowledge nor parameter tuning. The first method, HDBSCAN, is fast—relative to other popular clustering methods—and is able to group unstructured or intrinsically disordered systems (such as intrinsically disordered proteins, or IDPs) into bins that represent global conformational shifts. HDBSCAN is also useful for determining the overall stability of a system—as it tends to group stable systems into one or two bins—and identifying transition events between metastable states. The second method, iMWK-Means, with explicit rescaling followed by K-Means, while slower than HDBSCAN, performs well with stable, structured systems such as folded proteins and is able to identify higher resolution details such as changes in relative position of secondary structural elements. Used in conjunction, these clustering methods allow a user to discern quickly and without prior knowledge the stability of a simulated system and identify both local and global conformational changes.

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A topic discovery approach for unsupervised organization of legal document collections

2023

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ANALOC: Efficient analytics over Location Based Services

Cited by 10 publications

References 3 publications

A Systematic Approach For Data Cleansing Process of Geospatial Data to Perform Application Specific Data Analytics

A Systematic Approach For Data Cleansing Process of Geospatial Data to Perform Application Specific Data Analytics

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

A topic discovery approach for unsupervised organization of legal document collections

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