446 research outputs found
Query Stability in Monotonic Data-Aware Business Processes [Extended Version]
Organizations continuously accumulate data, often according to some business
processes. If one poses a query over such data for decision support, it is
important to know whether the query is stable, that is, whether the answers
will stay the same or may change in the future because business processes may
add further data. We investigate query stability for conjunctive queries. To
this end, we define a formalism that combines an explicit representation of the
control flow of a process with a specification of how data is read and inserted
into the database. We consider different restrictions of the process model and
the state of the system, such as negation in conditions, cyclic executions,
read access to written data, presence of pending process instances, and the
possibility to start fresh process instances. We identify for which facet
combinations stability of conjunctive queries is decidable and provide
encodings into variants of Datalog that are optimal with respect to the
worst-case complexity of the problem.Comment: This report is the extended version of a paper accepted at the 19th
International Conference on Database Theory (ICDT 2016), March 15-18, 2016 -
Bordeaux, Franc
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
Over the past years, there has been a resurgence of Datalog-based systems in
the database community as well as in industry. In this context, it has been
recognized that to handle the complex knowl\-edge-based scenarios encountered
today, such as reasoning over large knowledge graphs, Datalog has to be
extended with features such as existential quantification. Yet, Datalog-based
reasoning in the presence of existential quantification is in general
undecidable. Many efforts have been made to define decidable fragments. Warded
Datalog+/- is a very promising one, as it captures PTIME complexity while
allowing ontological reasoning. Yet so far, no implementation of Warded
Datalog+/- was available. In this paper we present the Vadalog system, a
Datalog-based system for performing complex logic reasoning tasks, such as
those required in advanced knowledge graphs. The Vadalog system is Oxford's
contribution to the VADA research programme, a joint effort of the universities
of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the
main contribution of this paper, we illustrate the first implementation of
Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive
termination control strategy. We also provide a comprehensive experimental
evaluation.Comment: Extended version of VLDB paper
<https://doi.org/10.14778/3213880.3213888
Recursive Rules with Aggregation: A Simple Unified Semantics
Complex reasoning problems are most clearly and easily specified using
logical rules, especially recursive rules with aggregation such as counts and
sums for practical applications. Unfortunately, the meaning of such rules has
been a significant challenge, leading to many different conflicting semantics.
This paper describes a unified semantics for recursive rules with
aggregation, extending the unified founded semantics and constraint semantics
for recursive rules with negation. The key idea is to support simple expression
of the different assumptions underlying different semantics, and orthogonally
interpret aggregation operations straightforwardly using their simple usual
meaning
Shared Arrangements: practical inter-query sharing for streaming dataflows
Current systems for data-parallel, incremental processing and view
maintenance over high-rate streams isolate the execution of independent
queries. This creates unwanted redundancy and overhead in the presence of
concurrent incrementally maintained queries: each query must independently
maintain the same indexed state over the same input streams, and new queries
must build this state from scratch before they can begin to emit their first
results. This paper introduces shared arrangements: indexed views of maintained
state that allow concurrent queries to reuse the same in-memory state without
compromising data-parallel performance and scaling. We implement shared
arrangements in a modern stream processor and show order-of-magnitude
improvements in query response time and resource consumption for interactive
queries against high-throughput streams, while also significantly improving
performance in other domains including business analytics, graph processing,
and program analysis
Tabling with Support for Relational Features in a Deductive Database
Tabling has been acknowledged as a useful technique in the logic programming arena for enhancing both performance and declarative properties of programs. As well, deductive database implementations benefit from this technique for implementing query solving engines. In this paper, we show how unusual operations in deductive systems can be integrated with tabling.Such operations come from relational database systems in the form of null-related (outer) joins, duplicate support and duplicate elimination. The proposal has been implemented as a proof of concept rather than an efficient system in the Datalog Educational System (DES) using Prolog as a development language and its dynamic database
Secure Time-Aware Provenance for Distributed Systems
Operators of distributed systems often find themselves needing to answer forensic questions, to perform a variety of managerial tasks including fault detection, system debugging, accountability enforcement, and attack analysis. In this dissertation, we present Secure Time-Aware Provenance (STAP), a novel approach that provides the fundamental functionality required to answer such forensic questions – the capability to “explain” the existence (or change) of a certain distributed system state at a given time in a potentially adversarial environment.
This dissertation makes the following contributions. First, we propose the STAP model, to explicitly represent time and state changes. The STAP model allows consistent and complete explanations of system state (and changes) in dynamic environments. Second, we show that it is both possible and practical to efficiently and scalably maintain and query provenance in a distributed fashion, where provenance maintenance and querying are modeled as recursive continuous queries over distributed relations. Third, we present security extensions that allow operators to reliably query provenance information in adversarial environments. Our extensions incorporate tamper-evident properties that guarantee eventual detection of compromised nodes that lie or falsely implicate correct nodes. Finally, the proposed research results in a proof-of-concept prototype, which includes a declarative query language for specifying a range of useful provenance queries, an interactive exploration tool, and a distributed provenance engine for operators to conduct analysis of their distributed systems. We discuss the applicability of this tool in several use cases, including Internet routing, overlay routing, and cloud data processing
IceDust: Incremental and Eventual Computation of Derived Values in Persistent Object Graphs
Derived values are values calculated from base values. They can be
expressed in object-oriented languages by means of getters calculating the derived value, and in relational or logic databases by means of (materialized) views. However, switching to a different calculation strategy (for example caching) in object-oriented programming requires invasive code changes, and the databases limit expressiveness by disallowing recursive aggregation.
In this paper, we present IceDust, a data modeling language for
expressing derived attribute values without committing to a calculation strategy. IceDust provides three strategies for calculating derived values in persistent object graphs: Calculate-on-Read, Calculate-on-Write, and Calculate-Eventually. We have developed a path-based abstract interpretation that provides static dependency analysis to generate code for these strategies. Benchmarks show that different strategies perform better in different scenarios. In addition we have conducted a case study that suggests that derived value calculations of systems used in practice can be expressed in IceDust
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