Mirta Baranović

Generating data quality rules and integration into ETL process

Many data quality projects are integrated into data warehouse projects without enough time allocated for the data quality part, which leads to a need for a quicker data quality process implementation that can be easily adopted as the first stage of data warehouse implementation. We will see that many data quality rules can be implemented in a similar way, and thus generated based on metadata tables that store information about the rules. These generated rules are then used to check data in designated tables and mark erroneous records, or to do certain updates of invalid data. We will also store information about the rules violations in order to provide analysis of such data. This could give a significant insight into our source systems. Entire data quality process will be integrated into ETL process in order to achieve load of data warehouse that is as automated, as correct and as quick as possible. Only small number of records would be left for manual inspection and reprocessing.

CASE STUDY BASED ON A MULTI-PERIOD MULTI-CRITERIA PRODUCTION PLANNING MODEL

The aim of the described model and procedures is to enable production planning by using multi-criteria linear programming in a manufacturing environment. It is assumed that linearity holds within acceptable tolerances and the problem of choice is present, i.e. the majority of products can be manufactured in a number of technically and financially different processes. The same resources, raw materials, machines, tools, and manpower can be assigned to different processes. The planning objectives include calculating the optimum production mix and achieving the capacity and material balance, while maximizing the contribution and minimizing the duration of the longest resource engagement. Multiple periods are applied to achieve approximated time scheduling. The described software also calculates the direct costs, enables easy locating of infeasibility cause and offers sensitivity analysis. A goal programming variation is introduced for maximum satisfaction of the market requirements. The described procedures are in practical use in a thermoplastics factory.

Geospatial data streams: Formal framework and implementation

Abstract A spatio-temporal database manages spatio-temporal objects and supports corresponding query languages. Today, the term moving objects databases is used as a synonym for spatio-temporal databases managing spatial objects with a continuously changing geospatial location and/or extent. Recent advances in wireless communication, miniaturization of spatially enabled devices and global navigation satellite systems (GNSS) services have resulted in a large number of novel application domains. Applications in these novel domains (geo-sensor networks, moving objects tracking, real-time traffic analysis, etc.) process huge volumes of continuous data streams, i.e. data sets that are produced incrementally over time, rather than those available in full before the processing begins. Several data stream management systems (DSMSs) have been developed to manage this data. Since they are mainly based on a relational paradigm, they do not support geospatial data. Therefore, there is an urgent need for geospatial data stream management, ranging from real-time monitoring and alerting to long-term analysis of processed geospatial data. In this paper we present a formal framework consisting of data types and operations needed to support geospatial data in data streams. It can be used as a basis either for implementation of a completely new geospatial DSMS, or for extending available open source products and research prototypes. We leverage the work on abstract data types from spatio-temporal databases, present an implementation based on user-defined aggregate functions and illustrate embedding into an SQL-like language.

Data warehouse as a part of the higher education information system in Croatia

Although the idea of a decision support system resourceful in querying and analysis can be traced back to the 1960s, it was not until the late 1990s that data warehousing was universally recognized and adopted. We address issues related to modelling and implementing a data warehouse for the higher education information system in Croatia. The purpose of the project is to provide a data querying service that will improve the understanding, planning and operational work of institutions of higher education in Croatia in order to facilitate and improve educational activities.

OCEANUS: a spatio-temporal data stream system prototype

Recent advances in wireless communication, miniaturization of spatially enabled devices and global navigation satellite systems (GNSS) services have resulted in a large number of novel application domains. Applications in these novel domains (moving objects tracking, sensor networks, fleet management, real-time intelligent transportation systems, etc.) process huge volume of continuous streaming data, i.e. data that is produced incrementally over time, rather than being available in full before processing. Data stream management systems (DSMS) have been developed to manage continuous data streams. Usually based on relational paradigm, they have rudimentary support for spatial data. Recent research efforts in data stream management systems focus mainly on processing continuous queries over traditional data streams, and only a few papers addressed spatio-temporal continuous queries. In this paper we present OCEANUS, an ongoing effort to extend TelegraphCQ DSMS with spatial support providing a platform for spatio-temporal streaming applications.

Data Types and Operations for Spatio-temporal Data Streams

While the field of spatio-temporal databases, moving objects databases and the field of data stream management systems have separately been rather extensively researched in the last decade, a combined field of spatio-temporal data stream management systems has only recently become a focus of serious research effort. A spatio-temporal data stream management system manages moving objects whose position and/or extent is provided by one or more data streams, and can be viewed as an extension of a moving objects database enabling it to manage data streams, or as an extension of a data stream management system enabling it to use spatial and spatio-temporal data. In this paper we present a data model, consisting of data types and operations on these data types, needed to support spatio-temporal data in a data stream management system. Presented data model is formally defined using many sorted algebra, and is illustrated through an SQL-like query language. It can be used as a basis either for implementation completely new spatio-temporal DSMS, or for extending available open source products and research prototypes, towards managing moving objects.

Scheduling of examination terms based on past experience

A faculty with a few thousand students, two fields of study and a few hundred subjects/specialisations faced the problem of how to devise their examination schedule. The aim of the examination schedule was to maximally disperse the examination terms as perceived by the students. A mixed-integer model turned out to be too complex, and therefore a genetic algorithm was applied. The achieved results were satisfactory enough to be accepted in the current academic year.

Business process management systems selection guidelines: Theory and practice

Nowadays many business process management systems (BPMSs) are available on the market and it is quite difficult to select the appropriate one. This paper presents a set of criteria and proposes guidelines for BPMS selection. The proposal has been developed on the basis of literature overview and the feedback from practical experience in BPM projects. The results of this research are valuable for both academic and business community.

Application of Machine Learning Algorithms for the Query Performance Prediction

This paper analyzes the relationship between the system load/throughput and the query response time in a real Online transaction processing (OLTP) system environment. Although OLTP systems are characterized by short transactions, which normally entail high availability and consistent short response times, the need for operational reporting may jeopardize these objectives. We suggest a new approach to performance prediction for concurrent database workloads, based on the system state vector which consists of 36 attributes. There is no bias to the importance of certain attributes, but the machine learning methods are used to determine which attributes better describe the behavior of the particular database server and how to model that system. During the learning phase, the systems profile is created using multiple reference queries, which are selected to represent frequent business processes. The possibility of the accurate response time prediction may be a foundation for automated decision-making for database (DB) query scheduling. Possible applications of the proposed method include adaptive resource allocation, quality of service (QoS) management or real-time dynamic query scheduling (e.g. estimation of the optimal moment for a complex query execution).

Generating spatio-temporal streaming trajectories

Recent research efforts in data stream management systems (DSMS) focus mainly on processing continuous queries over traditional data streams, and only a few addressed spatio-temporal continuous queries. OCEANUS presents an effort to extend TelegraphCQ DSMS with spatial support providing a platform for spatio-temporal streaming applications. Data type system that represents the formal basis for modeling moving objects in data streams, as well as an approach for managing moving objects in spatio-temporal data streams based on user-defined aggregate functions (UDAF) are presented. In this paper we are concerned with improving proposed approach for extracting moving objects out of spatio-temporal data streams based on UDAF. It is based on two methods for detecting static locations on trajectories of moving objects and for reducing total number of units in sliced representation by introducing trajectory buffer. First method leads to more precise results of certain operations over moving objects such as retrieving information about speed and direction, but also enables introduction of few new operations such as retrieving static locations and overall stoppage time on moving objects trajectory. Second method leads to better memory usage during processing spatio-temporal continuous queries in DSMS.