The pebble tree automaton and the pebble tree transducer are enhanced by additionally allowing an unbounded number of 'invisible' pebbles (as opposed to the usual 'visible' ones). The resulting pebble tree automata recognize the regular tree languages (i.e., can validate all generalized DTD's) and hence can find all matches of MSO definable n-ary patterns. Moreover, when viewed as a navigational device, they lead to an XPath-like formalism that has a path expression for every MSO definable binary pattern. The resulting pebble tree transducers can apply arbitrary MSO definable tests to (the observable part of) their configurations, they (still) have a decidable typechecking problem, and they can model the recursion mechanism of XSLT. The time complexity of the typechecking problem for conjunctive queries that use MSO definable binary patterns can often be reduced through the use of invisible pebbles.
Abstract-This paper describes the implementation of the OKE, which allows users other than root to load native and fully optimised code in the Linux kernel. Safety is guaranteed by trust management, language customisation and a trusted compiler. By coupling trust management with the compiler, the OKE is able to vary the level of restrictions on the code running in the kernel, depending on the programmer's privileges. Static sandboxing is used as much as possible to check adherence to the security policies at compile time.
F1 Query is a stand-alone, federated query processing platform that executes SQL queries against data stored in different filebased formats as well as different storage systems at Google (e.g., Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query eliminates the need to maintain the traditional distinction between different types of data processing workloads by simultaneously supporting: (i) OLTP-style point queries that affect only a few records; (ii) low-latency OLAP querying of large amounts of data; and (iii) large ETL pipelines. F1 Query has also significantly reduced the need for developing hard-coded data processing pipelines by enabling declarative queries integrated with custom business logic. F1 Query satisfies key requirements that are highly desirable within Google: (i) it provides a unified view over data that is fragmented and distributed over multiple data sources; (ii) it leverages datacenter resources for performant query processing with high throughput and low latency; (iii) it provides high scalability for large data sizes by increasing computational parallelism; and (iv) it is extensible and uses innovative approaches to integrate complex business logic in declarative query processing. This paper presents the end-to-end design of F1 Query. Evolved out of F1, the distributed database originally built to manage Google's advertising data, F1 Query has been in production for multiple years at Google and serves the querying needs of a large number of users and systems.
F1 is a distributed relational database system built at Google to support the AdWords business. F1 is a hybrid database that combines high availability, the scalability of NoSQL systems like Bigtable, and the consistency and usability of traditional SQL databases. F1 is built on Spanner, which provides synchronous cross-datacenter replication and strong consistency. Synchronous replication implies higher commit latency, but we mitigate that latency by using a hierarchical schema model with structured data types and through smart application design. F1 also includes a fully functional distributed SQL query engine and automatic change tracking and publishing.
Abstract. The OKE Corral is an active network environment which allows third-party active code to configure an active node's code organisation at any level, including the kernel. Using the safety properties of an open kernel environment and a simple 'Click-like' software model, third parties are able to load native code anywhere in the processing hierarchy and connect it to existing components at runtime.
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