A systematic review of provenance systems
Provenance refers to the entire amount of information, comprising all the elements and their relationships, that contribute to the existence of a piece of data. The knowledge of provenance data allows a great number of benefits such as verifying a ...
Sequential data classification by dynamic state warping
The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping ...
Hidden location prediction using check-in patterns in location-based social networks
Check-in facility in a location-based social network (LBSN) enables people to share location information as well as real-life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular ...
ACE-RL-Checkers: decision-making adaptability through integration of automatic case elicitation, reinforcement learning, and sequential pattern mining
In agents that operate in environments where decision-making needs to take into account, not only the environment, but also the minimizing actions of an opponent (as in games), it is fundamental that the agent is endowed with the ability of ...
A Bayesian approach to forecasting daily air-pollutant levels
Forecasting air-pollutant levels is an important issue, due to their adverse effects on public health, and often a legislative necessity. The advantage of Bayesian methods is their ability to provide density predictions which can easily be transformed ...
Time prediction on multi-perspective declarative business processes
Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very ...
Simulating outcomes of interventions using a multipurpose simulation program based on the evolutionary causal matrices and Markov chain
Predicting long-term outcomes of interventions is necessary for educational and social policy-making processes that might widely influence our society for the long term. However, performing such predictions based on data from large-scale experiments ...
Two collaborative filtering recommender systems based on sparse dictionary coding
This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user's future rating of a specific item. Secondly, a top-n recommender system which finds a ...
Learning sequential features for cascade outbreak prediction
Information cascades are ubiquitous in various online social networks. Outbreak of cascades could cause huge and unexpected effects. Therefore, predicting the outbreak of cascades at early stage is of vital importance to avoid potential bad effects and ...