2,850 research outputs found
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
Towards a Scalable Dynamic Spatial Database System
With the rise of GPS-enabled smartphones and other similar mobile devices,
massive amounts of location data are available. However, no scalable solutions
for soft real-time spatial queries on large sets of moving objects have yet
emerged. In this paper we explore and measure the limits of actual algorithms
and implementations regarding different application scenarios. And finally we
propose a novel distributed architecture to solve the scalability issues.Comment: (2012
ILARS: An Improved Empirical Analysis for Lars* Using Partitioning and Travel Penalty
In this paper we develop an improved web based location-aware recommender software system, ILARS, that uses location-based ratings to provide proper advice and counseling. Present recommender systems don’t consider about spatial attributes of users and also of items; But, ILARS*considers major classes regarding location such as spatial scores rate for the non-spatial things, non-spatial score rate for the spatial things, and spatial score rate for the spatial things. ILARS* deals with recommendation points for accomplishing user ranking locations with help of user partitioning methods, which that are spatially near querying users in an effective way that maximizes system computability by not reducing the systems quality. A style that supports recommendation successors nearer in travel distance to querying users is used by ILARS* to exploits item locations using travel penalty. For avoiding thorough access to any or all spatial things. ILARS* will apply these art singly, or based on the rating that is obtained. The experimental results show information from various location based social networks. Various social network tells that LARS* is magnified , most expanded ,inexpensive ,reasonable ,capable of showing recommendations which are accurate as compared to existing recommendation software systems.
DOI: 10.17762/ijritcc2321-8169.15073
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