Model-based Integration of Past & Future in TimeTravel

We demonstrate TimeTravel, an efficient DBMS system for seamless integrated querying of past and (forecasted) future values of time series, allowing the user to view past and future values as one joint time series. This functionality is important for advanced application domain like energy. The main idea is to compactly represent time series as models. By using models, the TimeTravel system answers queries approximately on past and future data with error guarantees (absolute error and confidence) one order of magnitude faster than when accessing the time series directly. In addition, it efficiently supports exact historical queries by only accessing relevant portions of the time series. This is unlike existing approaches, which access the entire time series to exactly answer the query. To realize this system, we propose a novel hierarchical model index structure. As real-world time series usually exhibits seasonal behavior, models in this index incorporate seasonality. To construct a hierarchical model index, the user specifies seasonality period, error guarantees levels, and a statistical forecast method. As time proceeds, the system incrementally updates the index and utilizes it to answer approximate and exact queries. TimeTravel is implemented into PostgreSQL, thus achieving complete user transparency at the query level. In the demo, we show the easy building of a hierarchical model index for a real-world time series and the effect of varying the error guarantees on the speed up of approximate and exact queries

A Rule-based Skyline Computation over a dynamic database

Skyline query which relies on the notion of Pareto dominance filters the data items from a database by ensuring only those data items that are not worse than any others are selected as skylines. However, the dynamic nature of databases in which their states and/or structures change throughout their lifetime to incorporate the current and latest information of database applications, requires a new set of skylines to be derived. Blindly computing skylines on the new state/structure of a database is inefficient, as not all the data items are affected by the changes. Hence, this paper proposes a rule-based approach in tackling the above issue with the main aim at avoiding unnecessary skyline computations. Based on the type of operation that changes the state/structure of a database, i.e. insert/delete/update a data item(s) or add/remove a dimension(s), a set of rules are defined. Besides, the prominent dominance relationships when pairwise comparisons are performed are retained; which are then utilised in the process of computing a new set of skylines. Several analyses have been conducted to evaluate the performance and prove the efficiency of our proposed solution

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Skyline Query Processing for Uncertain Data

Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over uncertain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continuous uncertain data, and propose a novel, efficient framework to answer these queries. Query answers are probabilistic, where each object is associated with a probability value of being a query answer. Typically, users specify a probability threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational overhead. Our framework employs an efficient two-phase query processing algorithm

Preference Query Evaluation over Expensive Attributes

Most database systems allow query processing over attributes that are derived at query runtime (e.g., user-defined functions and remote data calls to web services), making them expensive to compute relative to relational data stored in a heap or index. In addition, core support for efficient preference query processing has become an important objectiveindatabasesystems. Thispaperaddressesanimportant problemattheintersectionof thesetwoqueryprocessingobjectives: efficient preference query evaluation involving expensive attributes. We explore an efficient framework for processing skyline and multi-objective queries in a database when the data involves a mix of“cheap”and“expensive”attributes. Our solution involves a three-phase approach that evaluates a correct final preference answer while aiming to minimizing the number of expensive attributes computations. Unlike previous works for distributed preference algorithms that assume sorted access over each attribute, our framework assumes expensive attribute requests are stateless, i.e., know nothing previous requests. Thus, the proposed approachis more in line with realistic system architectures. Our framework is implemented inside the query processorofPostgreSQL,andevaluatedoverbothsyntheticand real data sets involving computation of expensive attributes over real web-service data (e.g., Microsoft MapPoint)

PrefJoin: An Efficient Preference-Aware Join Operator

Abstract—Preference queries are essential to a wide spectrum of applications including multi-criteria decision-making tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation, waiving on a wide set of queries that include preference computations over multiple relations. This paper presents PrefJoin, an efficient preferenceaware join query operator, designed specifically to deal with preference queries over multiple relations. PrefJoin consists of four main phases: Local Pruning, Data Preparation, Joining, and Refining that filter out, from each input relation, those tuples that are guaranteed not to be in the final preference set, associate meta data with each non-filtered tuple that will be used to optimize the execution of the next phases, produce a subset of join result that are relevant for the given preference function, and refine these tuples respectively. An interesting characteristic of PrefJoin is that it tightly integrates preference computation with join hence we can early prune those tuples that are guaranteed not to be an answer, and hence it saves significant unnecessary computations cost. PrefJoin supports a variety of preference function including skyline, multi-objective and k-dominance preference queries. We show the correctness of PrefJoin. Experimental evaluation based on a real system implementation inside PostgreSQL shows that PrefJoin consistently achieves from one to three orders of magnitude performance gain over its competitors in various scenarios. I