Abstract-The skyline query returns the most interesting tuples according to a set of explicitly defined preferences among attribute values. This work relaxes this requirement, and allows users to pose meaningful skyline queries without stating their choices. To compensate for missing knowledge, we first determine a set of uncertain preferences based on user profiles, i.e., information collected for previous contexts. Then, we define a probabilistic contextual skyline query (p-CSQ) that returns the tuples which are interesting with high probability. We emphasize that, unlike past work, uncertainty lies within the query and not the data, i.e., it is in the relationships among tuples rather than in their attribute values. Furthermore, due to the nature of this uncertainty, popular skyline methods, which rely on a particular tuple visit order, do not apply for p-CSQs. Therefore, we present novel non-indexed and index-based algorithms for answering p-CSQs. Our experimental evaluation concludes that the proposed techniques are significantly more efficient compared to a standard block nested loops approach.
Researchers have recognized the importance of utilizing temporal features for improving the performance of information retrieval systems. Specifically, the timeliness of a web document can be a significant factor for determining whether it is relevant for a search query. Previous works have proposed time-aware retrieval models with particular focus on news queries, where recent web documents related with a real-world event are generally preferable. These queries typically exhibit bursts in the volume of published documents or submitted queries. However, no work has studied the role of time in queries such as "credit card overdraft fees" that have no major spikes in either document or query volumes over time, yet they still favor more recently published documents. In this work, we focus on this class of queries that we refer to as "timely queries". We show that the change in the terms distribution of results of timely queries over time is strongly correlated with the users' perception of time sensitivity. Based on this observation, we propose a method to estimate the query timeliness requirements and we propose principled ways to incorporate document freshness into the ranking model. Our study shows that our method yields a more accurate estimation of timeliness compared to volume-based approaches. We experimentally compare our ranking strategy with other time-sensitive and non time-sensitive ranking algorithms and we show that it improves the results' retrieval quality for timely queries.
<div class="section abstract"><div class="htmlview paragraph">Catalytic aftertreatment is commonly used to reduce regulated gas emissions from Internal Combustion (IC) engines. Achieving fast catalyst light-off has always been a challenge for automobile IC engine applications. This paper experimentally studied the thermal management and regulated gas emissions from a Spark Ignition (SI) engine with Dynamic Skip Fire (DSF®) technology during cold start period.</div><div class="htmlview paragraph">The study has found that DSF can increase exhaust gas temperature at the catalyst inlet by up to 100°C, and the exhaust enthalpy by up to 20%. Cold start tailpipe carbon monoxide (CO) and hydrocarbon (HC) emissions can be reduced by 10% to 20% largely due to the increased exhaust gas temperature and enthalpy. Dynamic air pumping can further increase exhaust gas temperature by 30 °C, and can nearly double enthalpy delivered to the catalyst, which reduces cold start HC emissions by more than 50%.</div></div>
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