Computer Science - Databases Publications (50)


Computer Science - Databases Publications

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. Read More

We present a new class of service for location based social networks, called the Flexible Group Spatial Keyword Query, which enables a group of users to collectively find a point of interest (POI) that optimizes an aggregate cost function combining both spatial distances and keyword similarities. In addition, our query service allows users to consider the tradeoffs between obtaining a sub-optimal solution for the entire group and obtaining an optimimized solution but only for a subgroup. We propose algorithms to process three variants of the query: (i) the group nearest neighbor with keywords query, which finds a POI that optimizes the aggregate cost function for the whole group of size n, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup and a POI that optimizes the aggregate cost function for a given subgroup size m (m <= n), and (iii) the multiple subgroup nearest neighbor with keywords query, which finds optimal subgroups and corresponding POIs for each of the subgroup sizes in the range [m, n]. Read More

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. Because it offers low responses times, Product Quantization (PQ) is a popular solution. PQ compresses high-dimensional vectors into short codes using several sub-quantizers, which enables in-RAM storage of large databases. Read More

Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in urban environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e. Read More

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Read More

Aggregation and visualization of geographical data are an important part of environmental data mining, environmental modelling, and agricultural management. However, it is difficult to aggregate geospatial data of the various formats, such as maps, census and survey data. This paper presents a framework named PlaniSphere, which can aggregate the various geospatial datasets, and synthesizes raw data. Read More

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems. Read More

Duplication, whether exact or partial, is a common issue in many datasets. In clinical notes data, duplication (and near duplication) can arise for many reasons, such as the pervasive use of templates, copy-pasting, or notes being generated by automated procedures. A key challenge in removing such near duplicates is the size of such datasets; our own dataset consists of more than 10 million notes. Read More

The AliEn (ALICE Environment) file catalogue is a global unique namespace providing mapping between a UNIX-like logical name structure and the corresponding physical files distributed over 80 storage elements worldwide. Powerful search tools and hierarchical metadata information are integral parts of the system and are used by the Grid jobs as well as local users to store and access all files on the Grid storage elements. The catalogue has been in production since 2005 and over the past 11 years has grown to more than 2 billion logical file names. Read More

In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes for query answers from databases. Read More

Determining if two sets are related - that is, if they have similar values or if one set contains the other - is an important problem with many applications in data cleaning, data integration, and information retrieval. A particularly popular metric that has been proposed is to measure the relatedness of two sets by treating the elements as vertices of a bipartite graph and calculating the score of the maximum matching pairing between elements. Compared to other metrics which require exact matchings between elements, this metric uses a similarity function to compare elements between the two sets, making it robust to small dissimilarities in elements and more useful for real-world, dirty data. Read More

Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. Read More

Efficient extraction of useful knowledge from these data is still a challenge, mainly when the data is distributed, heterogeneous and of different quality depending on its corresponding local infrastructure. To reduce the overhead cost, most of the existing distributed clustering approaches generate global models by aggregating local results obtained on each individual node. The complexity and quality of solutions depend highly on the quality of the aggregation. Read More

Net Asset Value (NAV) calculation and validation is the principle task of a fund administrator. If the NAV of a fund is calculated incorrectly then there is huge impact on the fund administrator; such as monetary compensation, reputational loss, or loss of business. In general, these companies use the same methodology to calculate the NAV of a fund, however the type of fund in question dictates the set of business rules used to validate this. Read More

GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query-Reverse k Nearest Neighbor Search over Trajectories (RkNNT), which can be used for route planning and capacity estimation. Read More

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of granularity, process discovery methods tend to generate overgeneralizing process models. Read More

Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input parameters. Read More

Building interactive tools to support data analysis is hard because it is not always clear what to build and how to build it. To address this problem, we present Precision Interfaces, a semi-automatic system to generate task-specific data analytics interfaces. Precision Interface can turn a log of executed programs into an interface, by identifying micro-variations between the programs and mapping them to interface components. Read More

R is a popular language and programming environment for data scientists. It is increasingly co-packaged with both relational and Hadoop-based data platforms and can often be the most dominant computational component in data analytics pipelines. Recent work has highlighted inefficiencies in executing R programs, both in terms of execution time and memory requirements, which in practice limit the size of data that can be analyzed by R. Read More

We consider the problem of duplicate detection: given a large data set in which each entry has multiple attributes, detect which distinct entries refer to the same real world entity. Our method consists of three main steps: creating a similarity score between entries, grouping entries together into `unique entities', and refining the groups. We compare various methods for creating similarity scores, considering different combinations of string matching, term frequency-inverse document frequency methods, and n-gram techniques. Read More

Cloud computing has allowed applications to allocate and elastically utilize massive amounts of resources of different types, leading to an exponential growth of the applications' configuration space and increased difficulty in predicting their performance. In this work, we describe a novel, automated profiling methodology that makes no assumptions on application structure. Our approach utilizes oblique Decision Trees in order to recursively partition an application's configuration space in disjoint regions, choose a set of representative samples from each subregion according to a defined policy and returns a model for the entire configuration space as a composition of linear models over each subregion. Read More

Quality control in crowdsourcing systems is crucial. It is typically done after data collection, often using additional crowdsourced tasks to assess and improve the quality. These post-hoc methods can easily add cost and latency to the acquisition process--particularly if collecting high-quality data is important. Read More

Living in the Information Age allows almost everyone have access to a large amount of information and options to choose from in order to fulfill their needs. In many cases, the amount of information available and the rate of change may hide the optimal and truly desired solution. This reveals the need of a mechanism that will highlight the best options to choose among every possible scenario. Read More

In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. Read More

Snapshot Isolation (SI) is a widely adopted concurrency control mechanism in database systems, which utilizes timestamps to resolve conflicts between transactions. However, centralized allocation of timestamps is a potential bottleneck for parallel transaction management. This bottleneck is becoming increasingly visible with the rapidly growing degree of parallelism of today's computing platforms. Read More

The article investigates classes of queries maintainable by conjunctive queries (CQs) and their extensions and restrictions in the dynamic complexity framework of Patnaik and Immerman. Starting from the basic language of quantifier-free conjunctions of positive atoms, it studies the impact of additional operators and features - such as union, atomic negation and quantification - on the dynamic expressiveness, for the standard semantics as well as for Delta-semantics. Although many different combinations of these features are possible, they basically yield five important fragments for the standard semantics, characterized by the addition of (1) arbitrary quantification and atomic negation, (2) existential quantification and atomic negation, (3) existential quantification, (4) atomic negation (but no quantification), and by (5) no addition to the basic language at all. Read More

More data is currently being collected and shared by software applications than ever before. In many cases, the user is asked if either all or none of their data can be shared. We hypothesize that in some cases, users would like to share data in more complex ways. Read More

Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Read More

The use of synthetic graph generators is a common practice among graph-oriented benchmark designers, as it allows obtaining graphs with the required scale and characteristics. However, finding a graph generator that accurately fits the needs of a given benchmark is very difficult, thus practitioners end up creating ad-hoc ones. Such a task is usually time-consuming, and often leads to reinventing the wheel. Read More

Many datasets have multiple tables connected by key-foreign key dependencies. Data scientists usually join all tables to bring in extra features from the so-called dimension tables. Unlike the statistical relational learning setting, such joins do not cause record duplications, which means regular IID models are typically used. Read More

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). Read More

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent users' query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Read More

Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. Read More

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. Read More

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 Relational Algebra as studied in the context of document spanners, Fagin et al.'s formal framework for information extraction. Read More

Nowadays, huge amounts of data are naturally collected in distributed sites due to different facts and moving these data through the network for extracting useful knowledge is almost unfeasible for either technical reasons or policies. Furthermore, classical par- allel algorithms cannot be applied, specially in loosely coupled environments. This requires to develop scalable distributed algorithms able to return the global knowledge by aggregating local results in an effective way. Read More

The data mining field is an important source of large-scale applications and datasets which are getting more and more common. In this paper, we present grid-based approaches for two basic data mining applications, and a performance evaluation on an experimental grid environment that provides interesting monitoring capabilities and configuration tools. We propose a new distributed clustering approach and a distributed frequent itemsets generation well-adapted for grid environments. Read More

The universally applied Codd's relational model has two constructs: a stored relation, with stored attributes only and a view, only with the inherited ones. In 1992, we have proposed third construct, mixing both types of attributes. Examples showed the idea attractive. Read More

XML queries can be modeled by twig pattern queries (TPQs) specifying predicates on XML nodes and XPath relationships satisfied between them. A lot of TPQ types have been proposed; this paper takes into account a TPQ model extended by a specification of output and non-output query nodes since it complies with the XQuery semantics and, in many cases, it leads to a more efficient query processing. In general, there are two approaches to process the TPQ: holistic joins and binary joins. Read More

As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will invoke the appropriate algorithms and system configurations to execute it. An important observation towards designing such a framework is that many ML tasks can be expressed as mathematical optimization problems, which take a specific form. Read More

Assessing and improving the quality of data are fundamental challenges for data-intensive systems that have given rise to applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data migration are now reasonably well understood in isolation, not much attention has been given to the interplay between the tools addressing issues in these areas. We focus on the problem of determining whether the available data-processing procedures can be used together to bring about the desired quality of the given data. Read More

In visual exploration and analysis of data, determining how to select and transform the data for visualization is a challenge for data-unfamiliar or inexperienced users. Our main hypothesis is that for many data sets and common analysis tasks, there are relatively few "data slices" that result in effective visualizations. By focusing human users on appropriate and suitably transformed parts of the underlying data sets, these data slices can help the users carry their task to correct completion. Read More

The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. Read More

This paper provides a survey of the current state of the art in Causally-Consistent data stores. Furthermore, we present the design of Thespis, a middleware that innovatively leverages the Actor model to implement causal consistency over an industry-standard data store. Read More

Cohesive subgraph detection is an important graph problem that is widely applied in many application domains, such as social community detection, network visualization, and network topology analysis. Most of existing cohesive subgraph metrics can guarantee good structural properties but may cause the free-rider effect. Here, by free-rider effect, we mean that some irrelevant subgraphs are combined as one subgraph if they only share a small number of vertices and edges. Read More

A massive amount of data generated today on platforms such as social networks, telecommunication networks, and the internet in general can be represented as graph streams. Activity in a network's underlying graph generates a sequence of edges in the form of a stream; for example, a social network may generate a graph stream based on the interactions (edges) between different users (nodes) over time. While many graph mining algorithms have already been developed for analyzing relatively small graphs, graphs that begin to approach the size of real-world networks stress the limitations of such methods due to their dynamic nature and the substantial number of nodes and connections involved. Read More

Most modern data stores tend to be distributed, to enable the scaling of the data across multiple instances of commodity hardware. Although this ensures a near unlimited potential for storage, the data itself is not always ideally partitioned, and the cost of a network round-trip may cause a degradation of end-user experience with respect to response latency. The problem being solved is bringing the data objects closer to the frequent sources of requests using a dynamic repartitioning algorithm. Read More

In this paper we present a new error bound on sampling algorithms for frequent itemsets mining. We show that the new bound is asymptotically tighter than the state-of-art bounds, i.e. Read More

The need for modern data analytics to combine relational, procedural, and map-reduce-style functional processing is widely recognized. State-of-the-art systems like Spark have added SQL front-ends and relational query optimization, which promise an increase in expressiveness and performance. But how good are these extensions at extracting high performance from modern hardware platforms? While Spark has made impressive progress, we show that for relational workloads, there is still a significant gap compared with best-of-breed query engines. Read More

Codd's relational model describes just one possible world. To better cope with incomplete information, extended database models allow several possible worlds. Vague tables are one such convenient extended model where attributes accept sets of possible values (e. Read More