Friendship prediction model based on factor graphs integrating geographical location

With the development of network services and location-based systems,many mobile applications begin to use users’geographical location to provide better services.In terms of social networks,geographical location is actively shared by users.In some applications with recommendation services,before the geographical location recommendation is provided,the authors have to obtain user’s permission.This kind of social network integrated with geographical location information is called location-based social networks(abbreviate for LBSNs).In the LBSN,each user has location information when he or she checked in hotels or feature spots.Based on this information,they can identify user’s trajectory of movement behaviour and activity patterns.In general,if there is friendship between two users,their trajectories in reality are likely to be similar.In this study,according to user’s geographical location information over a period of time,they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs.In particular,they propose a new factor function and a factor graph model based on user’s geographical location to predict the friendship between two users in the real LBSN.

A BiLSTM cardinality estimator in complex database systems based on attention mechanism

An excellent cardinality estimation can make the query optimiser produce a good execution plan.Although there are some studies on cardinality estimation,the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as well.In particular,they are difficult to accurately obtain the complex relationships between multiple tables in complex database systems.When dealing with complex queries,the existing cardinality estimators cannot achieve good results.In this study,a novel cardinality estimator is proposed.It uses the core techniques with the BiLSTM network structure and adds the attention mechanism.First,the columns involved in the query statements in the training set are sampled and compressed into bitmaps.Then,the Word2vec model is used to embed the word vectors about the query statements.Finally,the BiLSTM network and attention mechanism are employed to deal with word vectors.The proposed model takes into consideration not only the correlation between tables but also the processing of complex predicates.Extensive experiments and the evaluation of BiLSTM-Attention Cardinality Estimator(BACE)on the IMDB datasets are conducted.The results show that the deep learning model can significantly improve the quality of cardinality estimation,which is a vital role in query optimisation for complex databases.

Key techniques for predicting the uncertain trajectories of moving objects with dynamic environment awareness

Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks（CTBNs） is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.

Survey on vehicle map matching techniques

With the development of location‐based services and Big data technology,vehicle map matching techniques are growing rapidly,which is the fundamental techniques in the study of exploring global positioning system(GPS)data.The pre‐processed GPS data can provide the guarantee of high‐quality data for the research of mining passenger’s points of interest and urban computing services.The existing surveys mainly focus on map‐matching algorithms,but there are few descriptions on the key phases of the acquisition of sampling data,floating car and road data preprocessing in vehicle map matching systems.To address these limitations,the contribution of this survey on map matching techniques lies in the following aspects:(i)the background knowledge,function and system framework of vehicle map matching techniques;(ii)description of floating car data and road network structure to understand the detailed phase of map matching;(iii)data preprocessing rules,specific methodologies,and significance of floating car and road data;(iv)map matching algorithms are classified by the sampling frequency and data information.The authors give the introduction of open‐source GPS sampling data sets,and the evaluation measurements of map‐matching approaches;(v)the suggestions on data preprocessing and map matching algorithms in the future work.

Cooperation Oriented Computing:A Computing Model Based on Emergent Dynamics of Group Cooperation

Traditional way of problem solving tries to deliver data to program.But when the problem’s complexity exponentially increases as the data scale increases,to obtain the solution is difficult.Group cooperation computing model works in an inverse way by delivering program to data.It first models each single data as individual and data unit as group of individuals.Then,different cooperation rules are designed for individuals to cooperate with each other.Finally,the solution of the problem emerges through individuals’cooperation process.This study applies group cooperation computing model to solve Hamilton Path problem which has NP-complete time complexity.Experiment results show that the cooperation model works much better than genetic algorithm.More importantly,the following properties of group cooperation computing are found which may be different from the traditional computing theory.(1)By using different cooperation rules,the same problem with the same scale may exhibit different complexities,such as liner or exponent.(2)By using the same cooperation rule,when the problem scale is less than a specific threshold,the problem’s time complexity is liner.Otherwise,the problem complexity may be exponent.