Foundations of Declarative Data Analysis Using Limit Datalog Programs

Motivated by applications in declarative data analysis, we study $\mathit{Datalog}_{\mathbb{Z}}$---an extension of positive Datalog with arithmetic functions over integers. This language is known to be undecidable, so we propose two fragments. In $\mathit{limit}~\mathit{Datalog}_{\mathbb{Z}}$ predicates are axiomatised to keep minimal/maximal numeric values, allowing us to show that fact entailment is coNExpTime-complete in combined, and coNP-complete in data complexity. Moreover, an additional $\mathit{stability}$ requirement causes the complexity to drop to ExpTime and PTime, respectively. Finally, we show that stable $\mathit{Datalog}_{\mathbb{Z}}$ can express many useful data analysis tasks, and so our results provide a sound foundation for the development of advanced information systems.Comment: 23 pages; full version of a paper accepted at IJCAI-17; v2 fixes some typos and improves the acknowledgment

Stratified Negation in Limit Datalog Programs

There has recently been an increasing interest in declarative data analysis, where analytic tasks are specified using a logical language, and their implementation and optimisation are delegated to a general-purpose query engine. Existing declarative languages for data analysis can be formalised as variants of logic programming equipped with arithmetic function symbols and/or aggregation, and are typically undecidable. In prior work, the language of $\mathit{limit\ programs}$ was proposed, which is sufficiently powerful to capture many analysis tasks and has decidable entailment problem. Rules in this language, however, do not allow for negation. In this paper, we study an extension of limit programs with stratified negation-as-failure. We show that the additional expressive power makes reasoning computationally more demanding, and provide tight data complexity bounds. We also identify a fragment with tractable data complexity and sufficient expressivity to capture many relevant tasks.Comment: 14 pages; full version of a paper accepted at IJCAI-1

Declarative Data Analytics: a Survey

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.Comment: 36 pages, 2 figure

ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)

We present ExplainIt!, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. ExplainIt! then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how ExplainIt! had helped us resolve over 30 performance issues in a commercial product since late 2014, of which we discuss a few cases in detail.Comment: SIGMOD Industry Track 201

Data-Oriented Declarative Language for Optimizing Business Processes

There is a signifi cant number of declarative languages to describe business processes. They tend to be used when business processes need to be fl exible and adaptable, being not possible to use an imperative description. Declarative languages in business process have been traditionally used to describe the order of activities, specifi cally the order allowed or prohibited. Unfortunately, none of them is worried about a declarative description of exchanged data between the activities and how they can infl uence the model. In this paper, we analyse the data description capacity of a variety of declarative languages in business processes. Using this analysis, we have detected the necessity to include data exchanged aspects in the declarative descriptions. In order to solve the gap, we propose a Data-Oriented Optimization Declarative LanguagE, called DOODLE, which includes the process requirements referred to data description, and the possibility to include an optimization function about the process output data

Kesantunan Deklaratif dalam Kegiatan Webinar Pendidikan “Peran Guru dalam Mengembangkan Pembelajaran Jarak Jauh Menyikapi New Normal #1” di Youtube

Webinars are seminars or meetings held virtually using certain internet-based applications. In participating in webinar activities, many types of speech that can be used by webinar participants, one of which is declarative speech. Declarative speech can be examined from various aspect, including aspect of function and the principle of politeness. This study aims to describe, analyze, and interpret declarative politeness in the educational webinar activity “The Role pf Teachers in Developing Distance Learning in the Respecting the New Normal #1” on Youtube. This research uses a descriptive method. The data analysis technique in this study used content analysis techniques. Data collection techniques used in this study were documentation techniques, listening techniques, and note-taking techniques. The data in this study are in the form of declarative speech spoken by the webinar participants. Sources of data in this study are the entire speeches of the educational webinar participants “The Role of Teachers in Developing Distance Learning in Respecting New Normal #1” which was broadcast live on June 17, 2020. The results of this study indicate the following. First, the most declarative function found in the speeches of webinar participants in educational webinar activities is the function of declaring information. Based on the data analysis of the declarative speech function, the function of declaring information is widely found because most of the contents of this webinar activity are providing information or matters relating to information about the role of teachers in developing distance learning. Second, the maxims of politeness principles found in the declarative utterances of webinar participants in educational webinar activities are maxim of appreciation and maxim of sympathy. Based on the data analysis, the maxim of politeness principle, the maxim of appreciation and the maxim of sympathy were found because the webinar participants were more likely to show respect and sympathy for the speech partner

Acquisition and Declarative Analytical Processing of Spatio-Temporal Observation Data

A generic framework for spatio-temporal observation data acquisition and declarative analytical processing has been designed and implemented in this Thesis. The main contributions of this Thesis may be summarized as follows: 1) generalization of a data acquisition and dissemination server, with great applicability in many scientific and industrial domains, providing flexibility in the incorporation of different technologies for data acquisition, data persistence and data dissemination, 2) definition of a new hybrid logical-functional paradigm to formalize a novel data model for the integrated management of entity and sampled data, 3) definition of a novel spatio-temporal declarative data analysis language for the previous data model, 4) definition of a data warehouse data model supporting observation data semantics, including application of the above language to the declarative definition of observation processes executed during observation data load, and 5) column-oriented parallel and distributed implementation of the spatial analysis declarative language. The huge amount of data to be processed forces the exploitation of current multi-core hardware architectures and multi-node cluster infrastructures