Tink: a temporal graph analytics library for Apache Flink

We introduce the Tink library for distributed temporal graph analytics. Increasingly, reasoning about temporal aspects of graph-structured data collections is an important aspect of analytics. For example, in a communication network, time plays a fundamental role in the propagation of information within the network. Whereas existing tools for temporal graph analysis are built stand alone, Tink is a library in the Apache Flink ecosystem, thereby leveraging its advanced mature features such as distributed processing and query optimization. Furthermore, Flink requires little effort to process and clean the data without having to use different tools before analyzing the data. Tink focuses on interval graphs in which every edge is associated with a starting time and an ending time. The library provides facilities for temporal graph creation and maintenance, as well as standard temporal graph measures and algorithms. Furthermore, the library is designed for ease of use and extensibility

Tink:a temporal graph analytics library for Apache Flink

We introduce the Tink library for distributed temporal graph analytics. Increasingly, reasoning about temporal aspects of graph-structured data collections is an important aspect of analytics. For example, in a communication network, time plays a fundamental role in the propagation of information within the network. Whereas existing tools for temporal graph analysis are built stand alone, Tink is a library in the Apache Flink ecosystem, thereby leveraging its advanced mature features such as distributed processing and query optimization. Furthermore, Flink requires little effort to process and clean the data without having to use different tools before analyzing the data. Tink focuses on interval graphs in which every edge is associated with a starting time and an ending time. The library provides facilities for temporal graph creation and maintenance, as well as standard temporal graph measures and algorithms. Furthermore, the library is designed for ease of use and extensibility

Condition on arrival of transferred critically ill patients

We performed a retrospective inventory of the condition of transferred patients to our 11-bed medical ICU, aimed firstly to measure the quality of these transports and secondly to identify variables that may predict a high risk of deterioration during transferral. By a search in our hospital database, we identified 112 consecutive patients (47 women/65 men) transferred from other hospitals (distance 20-350 km) to our I[CU over a period of 14 months. The following data were collected on departure (if available) and on arrival: blood pressure, heart rate, temperature, oxygen saturation, routine laboratory parameters, arterial blood gas analysis, lactic acid, settings of mechanical ventilation, use of vasopressor/inotropic medication, presence of venous and arterial catheters and Apache II score on arrival. No major worsening during transportation was found, looking at the whole group. However, individual data showed severe deterioration of some patients during transport. We were not able to point out parameters that could predict hemodynamic or respiratory instability during transport or condition on arrival. In conclusion, quality of transport seems fairly good; in individual cases, improvements are possible. Therefore, we plan to investigate whether or not a strict protocol, based on recommendations in the literature and on local feasibility can further improve condition on arrival and survival of transferred ICU patients in our adherence region. (See Editorial p. 177) (C) 2000 Elsevier Science B.V. All rights reserved