We consider fairness in dimensionality reduction. Nonlinear dimensionality reduction yields low dimensional representations that let users visualize and explore high-dimensional data. However, traditional dimensionality reduction may yield biased visualizations overemphasizing relationships of societal phenomena to sensitive attributes or protected groups. We introduce a framework of fair neighbor embedding, the Fair Neighbor Retrieval Visualizer, which formulates fair nonlinear dimensionality reduction as an information retrieval task whose performance and fairness are quantified by information retrieval criteria. The method optimizes low-dimensional embeddings that preserve high-dimensional data neighborhoods without yielding biased association of such neighborhoods to protected groups. In experiments the method yields fair visualizations outperforming previous methods.

In graph data, each node often serves multiple functionalities. However, most graph embedding models assume that each node can only possess one representation. We address this issue by proposing a nonparametric graph embedding model. The model allows each node to learn multiple representations where they are needed to represent the complexity of random walks in the graph. It extends the Exponential family graph embedding model with two nonparametric prior settings, the Dirichlet process and the uniform process. The model combines the ability of Exponential family graph embedding to take the number of occurrences of context nodes into account with nonparametric priors giving it the flexibility to learn more than one latent representation for each node. The learned embeddings outperform other state of the art approaches in link prediction and node classification tasks.

Abstract We present a search system for grammatically analyzed corpora of Finnish parliamentary records and interviews with former parliamentarians, annotated with metadata of talk structure and involved parliamentarians, and discuss their use through carefully chosen digital humanities case studies. We first introduce the construction, contents, and principles of use of the corpora. Then we discuss the application of the search system and the corpora to study how politicians talk about power, how ideological terms are used in political speech, and how to identify narratives in the data. All case studies stem from questions in the humanities and the social sciences, but rely on the grammatically parsed corpora in both identifying and quantifying passages of interest. Finally, the paper discusses the role of natural language processing methods for questions in the (digital) humanities. It makes the claim that a digital humanities inquiry of parliamentary speech and interviews with politicians cannot only rely on computational humanities modeling, but needs to accommodate a range of perspectives starting with simple searches, quantitative exploration, and ending with modeling. Furthermore, the digital humanities need a more thorough discussion about how the utilization of tools from information science and technologies alter the research questions posed in the humanities.

Abstract Recently, group recommendations have gained much attention. Nevertheless, most approaches consider only one round of recommendations. However, in a real-life scenario, it is expected that the history of previous recommendations is exploited to tailor the recommendations towards meeting the needs of the group members. Such history should include not only which items the system suggested, but also the reaction of the members to these items. This work introduces the problem of sequential group recommendations, by exploiting the concept of satisfaction and disagreement. Satisfaction describes how well the group received the suggested items. Disagreement describes the satisfaction bias among the group members. We utilize these concepts in three new aggregation methods, SDAA, SIAA and Average+, designed to address the specific challenges introduced by sequential group recommendations. We experimentally show the effectiveness of our methods using big real datasets for both stable and ephemeral groups.

Modern text data is increasingly gathered in situations where it is paired with a high-dimensional collection of covariates: then both the text, the covariates, and their relationships are of interest to analyze. Despite the growing amount of such data, current topic models are unable to take into account large amounts of covariates successfully: they fail to model structure among covariates and distort findings of both text and covariates. This paper presents a solution: a novel factor-topic model that enables researchers to analyze latent structure in both text and sophisticated document-level covariates collectively. The key innovation is that besides learning the underlying topical structure, the model also learns the underlying factorial structure from the covariates and the interactions between the two structures. A set of tailored variational inference algorithms for efficient computation are provided. Experiments on three different datasets show the model outperforms comparable topic models in the ability to predict held-out document content. Two case studies focusing on Finnish parliamentary election candidates and game players on Steam demonstrate the model discovers semantically meaningful topics, factors, and their interactions. The model both outperforms state-of-the-art models in predictive accuracy and offers new factor-topic insights beyond other topic models.

Nonnegative matrix factorization (NMF) is a popular approach to model data, however, most models are unable to flexibly take into account multiple matrices across sources and time or apply only to integer-valued data. We introduce a probabilistic, Gaussian Process-based, more inclusive NMF-based model which jointly analyzes nonnegative data such as text data word content from multiple sources in a temporal dynamic manner. The model collectively models observed matrix data, source-wise latent variables, and their dependencies and temporal evolution with a full-fledged hierarchical approach including flexible nonparametric temporal dynamics. Experiments on simulated data and real data show the model out-performs, comparable models. A case study on social media and news demonstrates the model discovers semantically meaningful topical factors and their evolution

The widespread use of information systems has become a valuable source of semi-structured data. In this context, Entity Resolution (ER) emerges as a fundamental task to integrate multiple knowledge bases or identify similarities between data items (i.e., entities). Since ER is an inherently quadratic task, blocking techniques are often used to improve efficiency. Beyond the challenges related to the data volume and heterogeneity, blocking techniques also face two other challenges: streaming data and incremental processing. To address these challenges, we propose PRIME, a novel incremental schema-agnostic blocking technique that utilizes parallelism to enhance blocking efficiency. The proposed technique deals with streaming and incremental data using a distributed computational infrastructure. To improve efficiency, the technique avoids unnecessary comparisons and applies a time window strategy to prevent excessive memory consumption.

Increasing the on-time rate of bus service can prompt the people’s willingness to travel by bus, which is an effective measure to mitigate the city traffic congestion. Performing queries on the bus arrival can be used to identify and analyze various kinds of non-on-time events that happened during the bus journey, which is helpful for detecting the factors of delaying events, and providing decision support for optimizing the bus schedules. We propose a data management model, called Bus-OLAP, for querying bus journey data, considering the characteristics of bus running and the scenarios of non-on-time analysis. While fulfilling typical requirements of bus journey data queries, Bus-OLAP not only provides a flexible way to manage the data and to implement multiple granularity data query and update, but it also supports distributed queries and computation. The experiments on real-world bus journey data verify that Bus-OLAP is effective and efficient.

In this paper, a new multi-attribute and high capacity image-based watermarking technique for relational data is proposed. The embedding process causes low distortion into the data considering the usability restrictions defined over the marked relation. The conducted experiments show the high resilience of the proposed technique against tuple deletion and tuple addition attacks. An interesting trend of the extracted watermark is analyzed when, within certain limits, if the number of embedded marks is small, the watermark signal far from being compromised, discretely improves in the case of tuple addition attacks. According to the results, marking 13% of the attributes and under an attack of 100% of tuples addition, 96% of the watermark is extracted. Also, while previous techniques embed up to 61% of the watermark, under the same conditions, we guarantee to embed 99.96% of the marks.

This paper describes the design of traits, abstract superclasses, in the verification-aware programming language Dafny. Although there is no inheritance among classes in Dafny, the traits make it possible to describe behavior common to several classes and to write code that abstracts over the particular classes involved. The design incorporates behavioral specifications for a trait's methods and functions, just like for classes in Dafny. The design has been implemented in the Dafny tool.

Good software development and particularly maintenance practices form an important factor for success in software business. If one wants to constantly produce new successful releases of the applications, a proper efficient software maintenance process is the key. In this work, we study data from mobile application maintenance to understand and conceptualize how mobile application maintenance takes place. Based on the data on release history, we deduce different mobile application maintenance models from the perspectives of maintenance scheduling and maintenance requirements.

Rights exporting plays an essential role in the battle of fighting for Digital Rights Management (DRM) interoperability. The decision making process determines the results of rights exporting. In order to achieve optimal results in rights exporting, we leverage the process with algorithms for rights adaptation and rights decomposition. We also demonstrate how the proposed process can lead to improved results.

The incompatibility of various Digital Rights Management (DRM) systems remain as an obstacle hampering user experience and business on DRM based services. To increase DRM interoperability, rights exporting currently seems a preferred solution. Based on an earlier DRM model [8,9], we extend and consolidate the concept of rights adaptation in rights exporting. We analyse different ways of adapting rights to be exported and present a general rights adaptation framework.

Traditionally, data used in OLAP (online analytical processing) have been limited to the contents of the data warehouse of a company. However, the needs for analysis are often more demanding and data are needed from different sources. In this article, we study how the semantics of data sources can be described to allow combining data from several sources into an OLAP cube. We apply Semantic Web technologies for defining an OWL/RDF ontology for OLAP data sources and OLAP cubes. These definitions are then utilised in OLAP cube formation by posing an OWL/RDF ontology-based query against them. We use Grid technologies to enhance the efficiency of processing and ensuring security. Our primary interest is in the cube construction (i.e., ETL process), and we assume that standard OLAP methods can be used for the actual analysis. Our tests show that the proposed approach can speed up the construction of an OLAP cube for ad hoc queries by supporting a high-level query language and reducing the amount of required data.

On-Line Analytical Processing (OLAP) is a powerful method for analysing large data warehouse data. Typically, the data for an OLAP database is collected from a set of data repositories such as e.g. operational databases. This data set is often huge, and it may not be known in advance what data is required and when to perform the desired data analysis tasks. Sometimes it may happen that some parts of the data are only needed occasionally. Therefore, keeping the OLAP database constantly up-to-date is not only a highly demanding task but it also may be overkill in practice.This suggests that in some applications it would be more feasible to form the OLAP cubes only when they are actually needed. We present such a system. As the data sources may well be heterogeneous, we propose an XML language for data collection. Our system also has a facility, where the user may pose a query against a universal OLAP cube using the MDX language. The query is analysed to determine which data is required for the desired OLAP cube.

An On-Line Analytical Processing (OLAP) user often follows a train of thought, posing a sequence of related queries against the data warehouse. Although their details are not known in advance, the general form of those queries is apparent beforehand. Thus, the user can outline the relevant portion of the data posing generalised queries against a cube representing the data warehouse.Since existing OLAP design methods are not suitable for non-professionals, we present a technique that automates cube design given the data warehouse, functional dependency information, and sample OLAP queries expressed in the general form. The method constructs complete but minimal cubes with low risks related to sparsity and incorrect aggregations. After the user has given queries, the system will suggest a cube design. The user can accept it or improve it by giving more queries. The method is also suitable for improving existing cubes using respective real MDX queries.