The Internet of Things as a Privacy-Aware Database Machine

Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory

Panel on “Past and future of computer science theory”

The twenty-ninth edition of the SEBD (Italian Symposium on Advanced Database Systems), held on 5-9 September 2021 in Pizzo (Calabria Region, Italy), included a joint seminar on “Reminiscence of TIDB 1981” with invited talks given by some of the participants to the Advanced Seminar on Theoretical Issues in Databases (TIDB), which took place in the same region exactly forty years earlier. The joint seminar was concluded by a Panel on “The Past and the Future of Computer Science Theory” with the participation of four distinguished computer science theorists (Ronald Fagin, Georg Gottlob, Christos Papadimitriou and Moshe Vardi), who were interviewed by Giorgio Ausiello, Maurizio Lenzerini, Luigi Palopoli, Domenico Saccà and Francesco Scarcello. This paper reports the summaries of the four interviews

Pemodelan Graph Database Untuk Moda Transportasi Bus Rapid Transit

Bus Rapid Transit (BRT) merupakan salah satu alternatif transportasi massal. Rute BRT memiliki karakteristik khusus yang dapat dimodelkan dengan graph. Ketika rute dan shelter semakin bertambah, dibutuhkan aplikasi komputer untuk melakukan pencarian rute BRT. Hal tersebut akan memudahkan pencarian dan penjelajahan rute-rute BRT. Namun, ketika rute diimplementasikan menggunakan basis data relasional, performa query dapat menurun karena banyaknya operasi JOIN untuk mencari rute. Artikel ini mengusulkan sebuah model graph database untuk BRT dan implementasinya. Identifikasi kebutuhan data dilakukan, dilanjutkan dengan pemodelan menggunakan entity relationship (ER). Hasil ER tersebut kemudian dipetakan ke dalam property graph untuk kemudian diimplementasikan menggunakan produk graph database Neo4J. Hasil penelitian ini menunjukkan bahwa model yang dibuat bisa diterapkan dalam basis data graph dan graph dapat menunjukkan rute BRT tertentu. Dari sisi performance, basis data graph menunjukkan kinerja perambatan yang lebih baik dibandingkan dengan basis data relasional

PEMODELAN GRAPH DATABASE UNTUK MODA TRANSPORTASI BUS RAPID TRANSIT

Bus Rapid Transit (BRT) merupakan salah satu alternatif transportasi massal. Rute BRT memiliki karakteristik khusus yang dapat dimodelkan dengan graph. Ketika rute dan shelter semakin bertambah, dibutuhkan aplikasi komputer untuk melakukan pencarian rute BRT. Hal tersebut akan memudahkan pencarian dan penjelajahan  rute-rute BRT. Namun, ketika rute diimplementasikan menggunakan basis data relasional, performa query dapat menurun karena banyaknya operasi JOIN untuk mencari rute. Artikel ini mengusulkan sebuah model graph database untuk BRT dan implementasinya. Identifikasi kebutuhan data dilakukan, dilanjutkan dengan pemodelan menggunakan entity relationship (ER). Hasil  ER tersebut kemudian dipetakan ke dalam property graph untuk kemudian diimplementasikan menggunakan produk graph database Neo4J. Hasil penelitian ini menunjukkan bahwa model yang dibuat bisa diterapkan dalam basis data graph dan graph dapat menunjukkan rute BRT tertentu. Dari sisi performance, basis data graph menunjukkan kinerja perambatan yang lebih baik dibandingkan dengan basis data relasional. Keyword : BRT, graphdatabas

Rewriting Guarded Existential Rules into Small Datalog Programs

The goal of this paper is to understand the relative expressiveness of the query language in which queries are specified by a set of guarded (disjunctive) tuple-generating dependencies (TGDs) and an output (or \u27answer\u27) predicate. Our main result is to show that every such query can be translated into a polynomially-sized (disjunctive) Datalog program if the maximal number of variables in the (disjunctive) TGDs is bounded by a constant. To overcome the challenge that Datalog has no direct means to express the existential quantification present in TGDs, we define a two-player game that characterizes the satisfaction of the dependencies, and design a Datalog query that can decide the existence of a winning strategy for the game. For guarded disjunctive TGDs, we can obtain Datalog rules with disjunction in the heads. However, the use of disjunction is limited, and the resulting rules fall into a fragment that can be evaluated in deterministic single exponential time. We proceed quite differently for the case when the TGDs are not disjunctive and we show that we can obtain a plain Datalog query. Notably, unlike previous translations for related fragments, our translation requires only polynomial time if the maximal number of variables in the (disjunctive) TGDs is bounded by a constant

The Internet of Things as a Privacy-Aware Database Machine

Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory

Parallel-Correctness and Containment for Conjunctive Queries with Union and Negation

Single-round multiway join algorithms first reshuffle data over many servers and then evaluate the query at hand in a parallel and communication-free way. A key question is whether a given distribution policy for the reshuffle is adequate for computing a given query, also referred to as parallel-correctness. This paper extends the study of the complexity of parallel-correctness and its constituents, parallel-soundness and parallel-completeness, to unions of conjunctive queries with and without negation. As a by-product it is shown that the containment problem for conjunctive queries with negation is coNEXPTIME-complete