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In its traditional definition, a repair of an inconsistent database is a consistent database that differs from the inconsistent one in a “minimal way.” Often, repairs are not equally legitimate, as it is desired to prefer one over another; for example, one fact is regarded more reliable than another, or a more recent fact should be preferred to an earlier one. Motivated by these considerations, researchers have introduced and investigated the framework of preferred repairs, in the context of denial constraints and subset repairs. There, a priority relation between facts is lifted towards a priority relation between consistent databases, and repairs are restricted to the ones that are optimal in the lifted sense. Three notions of lifting (and preferred repairs) have been proposed: Pareto, global, and completion. In this manuscript, we investigate the complexity of three problems on preferred repairs. The first is the problem of deciding whether the priority relation contains enough information to clean the database unambiguously, or in other words, whether there is exactly one preferred repair. We show that the different lifting semantics entail highly different complexities for this problem. Then, we study the ability to quantify ambiguity, by investigating two classes of problems. The first is that of counting the preferred repairs. We establish a dichotomy in data complexity for the entire space of (sets of) functional dependencies for all three notions. The second class of problems is that of enumerating (i.e., generating) the preferred repairs. We devise enumeration algorithms with efficiency guarantees on the delay between generated repairs, even for constraints represented as general conflict graphs or hypergraphs.

Regular expressions with capture variables, also known as "regex formulas,'' extract relations of spans (interval positions) from text. These relations can be further manipulated via the relational Algebra as studied in the context of "document spanners," Fagin et al.'s formal framework for information extraction. We investigate the complexity of querying text by Conjunctive Queries (CQs) and Unions of CQs (UCQs) on top of regex formulas. Such queries have been investigated in prior work on document spanners, but little is known about the (combined) complexity of their evaluation. We show that the lower bounds (NP-completeness and W[1]-hardness) from the relational world also hold in our setting; in particular, hardness hits already single-character text. Yet, the upper bounds from the relational world do not carry over. Unlike the relational world, acyclic CQs, and even gamma-acyclic CQs, are hard to compute. The source of hardness is that it may be intractable to instantiate the relation defined by a regex formula, simply because it has an exponential number of tuples. Yet, we are able to establish general upper bounds. In particular, UCQs can be evaluated with polynomial delay, provided that every CQ has a bounded number of atoms (while unions and projection can be arbitrary). Furthermore, UCQ evaluation is solvable with FPT (Fixed-Parameter Tractable) delay when the parameter is the size of the UCQ.

We present alternative relatively simple and self-contained proofs of decidability of the emptiness problems for one-register alternating finite-memory automata and one-register alternating finite-memory tree automata.

Modern information extraction pipelines are typically constructed by (1) loading textual data from a database into a special-purpose application, (2) applying a myriad of text-analytics functions to the text, which produce a structured relational table, and (3) storing this table in a database. Obviously, this approach can lead to laborious development processes, complex and tangled programs, and inefficient control flows. Towards solving these deficiencies, we embark on an effort to lay the foundations of a new generation of text-centric database management systems. Concretely, we extend the relational model by incorporating into it the theory of document spanners which provides the means and methods for the model to engage the Information Extraction (IE) tasks. This extended model, called Spannerlog, provides a novel declarative method for defining and manipulating textual data, which makes possible the automation of the typical work method described above. In addition to formally defining Spannerlog and illustrating its usefulness for IE tasks, we also report on initial results concerning its expressive power.

Understanding movement of vehicles, people, goods, or any type of object is important for making knowledgeable decisions regarding public transportation planning. However, movement is a complex and dynamic phenomenon, and until recently, movement data was difficult to exploit for such planning purposes. The widespread adoption of location-aware devices such as Global Positioning System (GPS) sensors in public transportation systems and the adoption of open data principles have set the stage for new methods and tools for data collection and analysis of movement patterns. This paper illustrates the value and benefit of applying visual analytics techniques to movement data to create valuable insight for public transportation planning using vehicle-mounted devices on buses and trams. The contribution of the paper is three distinct visual analytics solutions that we developed using a real-world open data feed published by the Helsinki Public Transport Authority. The current work addresses encounters between objects, stops that interrupt movement, and flow dynamics of a large number of moving objects. We instantiated the described methods by showing that our findings can be applied in real-world use cases.

The framework of document spanners abstracts the task of information extraction from text as a function that maps every document (a string) into a relation over the document’s spans (intervals identified by their start and end indices). For instance, the regular spanners are the closure under the Relational Algebra (RA) of the regular expressions with capture variables, and the expressive power of the regular spanners is precisely captured by the class of vset-automata - a restricted class of transducers that mark the endpoints of selected spans. In this work, we embark on the investigation of document spanners that can annotate extractions with auxiliary information such as confidence, support, and confidentiality measures. To this end, we adopt the abstraction of provenance semirings by Green et al., where tuples of a relation are annotated with the elements of a commutative semiring, and where the annotation propagates through the (positive) RA operators via the semiring operators. Hence, the proposed spanner extension, referred to as an annotator, maps every string into an annotated relation over the spans. As a specific instantiation, we explore weighted vset-automata that, similarly to weighted automata and transducers, attach semiring elements to transitions. We investigate key aspects of expressiveness, such as the closure under the positive RA, and key aspects of computational complexity, such as the enumeration of annotated answers and their ranked enumeration in the case of numeric semirings. For a number of these problems, fundamental properties of the underlying semiring, such as positivity, are crucial for establishing tractability., LIPIcs, Vol. 155, 23rd International Conference on Database Theory (ICDT 2020), pages 8:1-8:18