NASA-Nearest Neighbour Algorithm with Structured Robustness Algorithm to improve Difficult Keyword Queries over Database

This is a technique to improve difficult keyword queries over databases.Estimating query performance is the job of predicting the excellence of results returned to examine in response of a query. Keyword queries on databases provide easy access to data, but often it goes through from low ranking quality. It is defined to get queries with low ranked result, quality to improve the user satisfaction. Post-retrieval predictors analyze the o of top-retrieved documents. This paper introduced a new technique to get high-performance named as NASA, this method is based on k-Nearest Neighbor (k-NN) search on the top-k results of the corrupted version of database. k-NN handles complex functions during the execution and it improve the loss of information. Simultaneously it helps to reduce the execution time. DOI: 10.17762/ijritcc2321-8169.15078

DCU-TCD@LogCLEF 2010: re-ranking document collections and query performance estimation

This paper describes the collaborative participation of Dublin City University and Trinity College Dublin in LogCLEF 2010. Two sets of experiments were conducted. First, different aspects of the TEL query logs were analysed after extracting user sessions of consecutive queries on a topic. The relation between the queries and their length (number of terms) and position (first query or further reformulations) was examined in a session with respect to query performance estimators such as query scope, IDF-based measures, simplified query clarity score, and average inverse document collection frequency. Results of this analysis suggest that only some estimator values show a correlation with query length or position in the TEL logs (e.g. similarity score between collection and query). Second, the relation between three attributes was investigated: the user's country (detected from IP address), the query language, and the interface language. The investigation aimed to explore the influence of the three attributes on the user's collection selection. Moreover, the investigation involved assigning different weights to the three attributes in a scoring function that was used to re-rank the collections displayed to the user according to the language and country. The results of the collection re-ranking show a significant improvement in Mean Average Precision (MAP) over the original collection ranking of TEL. The results also indicate that the query language and interface language have more in uence than the user's country on the collections selected by the users

Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizon