Integration of Public Information at the Regional Level Challenges and Opportunities *

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1 Integration of Public Information at the Regional Level Challenges and Opportunities * Leon Bobrowski, Mariusz Buzun, and Karol Przybszewski Faculty of Computer Science, Bialystok Technical University, Poland leon@ii.pb.bialystok.pl, {mbuzun,kprzybyszewski}@pbip.pl Introduction The Bulletin of Public Information (BIP) has been introduced by the Polish law in 2001 year as the mandatory manner of the official information publication on the web sites. This law put the publications obligations on the all administration units and on many enterprises. What is fundamentally important that information is collected in a standardized and documented manner in predefined points, which are well represented in the geographical information systems. Moreover, the same collection of information is measured in different times in a given point. In this way, a huge amount of a spatial, dynamic data is collected in the public databases. The collected public data can be used in modeling of a variety of activities in the Region and in their parameterization. Such parameterization of the region which is based on the reliable data gives a chance to reach the objectivity standards. It is particularly important in evaluations and comparisons of the region s activities at the national and European level. For example, new e-government services could be designed and objectively evaluated in this way. Podlasie - Region in Poland There are two hierarchical levels of administration in each voivodship in Poland. The voivodships are divided into the counties (pol. powiat) and the counties are divided into the communities (pol. gmina). The voivodships are governed by the voivode (the state administration) and by the marshal (the self-government administration). In Poland there are 16 voivodships. The Podlasie voivodship is divided into 17 counties and into 118 communities. An economic and technological status of the communities in the Region is varied. Generally, the Podlasie province does not belong to the richest or to the industrial regions in Poland. The Podlasie region is described as the green lungs of Poland with many forests and lakes. * This work was partially supported by the W/II/1/2004 and SPUB-M (COST 282) grants from the Białystok University of Technology. R. Traunmüller (Ed.): EGOV 2004, LNCS 3183, pp , Springer-Verlag Berlin Heidelberg 2004

2 530 Leon Bobrowski, Mariusz Buzun, and Karol Przybszewski Fig. 1. The map of the Podlasie region. Structure of the BIP Data The all 17 counties and 118 communities in the Podlasie region have their own Bulletin of Public Information (BIP). Apart of this, in the Region is functioning 194 Bulletins of other enterprises. The Center of Information Society Technology (CTSI) at the Computer Science Faculty TU is servicing actually the most (75 %) of the Bulletins in the region. The CTSI is servicing 50 % BIP s of public administration and 80 % BIP s of other entities. Servicing of so many BIP s by the CTSI Center increases chance for developing standards of the public data gathering and warehousing. The information published in the Bulletin by Public Administration Units (PAU) are divided into a following main categories:

3 Integration of Public Information at the Regional Level Challenges and Opportunities foreign and internal politics - internal organization of PAU - activity of PAU - PAU property - other public information Large part of the public data is collected in the text form. From the computational viewpoint it is convenient to represent such data in a standardized numerical form. We are assuming for this purpose the data representation in the form of the n- dimensional feature vectors and data matrices as it is done in the pattern recognition methods [1]. The j-th feature vector x j (t) = [x j1 (t),...,x jn (t)] T represents the state of the j-th object O j (t) (administration unit or enterprise) at the time moment t. The component x ji (t) of the vector x j (t) is a numerical result of the i-th feature (i =1,...,n) of a given object O j (t) (j = 1,..., m) characterized at the time moment t. We assume that the feature vectors x j (t) can be of a mixed type, because they contain both binary features (x i (t) {0,1}) as well as the real value (x i (t) R). The state of the all objects O j (t) at the time moment t is represented in the form of the data matrix X(t) with the m rows constituted by the dynamical feature vectors x j (t). X(t) = [x 1 (t), x 2 (t),..., x m (t)] T (1) The components x ji (t) of the feature vectors x j (t) and the data matrix X(t) are stochastic processes. It is natural to assume that each process x ji (t) is piecewise constant ( (j,i)) ( t: t k < t t k+1 ) x ji (t) = a k = const (2) where t k represents the k-th time moment (for example the k-th day) t 1 < t 2 <... < t K (3) The above assumption is justify, when for example we know that the changing of the BIP content can be done only ones per day. Designing e-services Based on Public Data The Internet services based on the public data of the e-government can be divided in the following categories which differs in respect to the levels of information aggregation: i. actual information about one administration unit ii. actual information about group of administration units iii. historical information about one administration unit iv. historical information about group of administration units The e-services of the first group (category i.) are the most popular in practice. This group includes the all e-services which facilitate findings of actual information related to only one administration unit. For example, an adequate e-service could support findings of the best free farms in a given county in particular price and facilities categories. The e-services of the second group (category ii.) have to include the interoperability mechanisms aimed at the effectively exchange of information between public administration units. The e-services of the second group could be seen roughly as the

4 532 Leon Bobrowski, Mariusz Buzun, and Karol Przybszewski services of the first group enriched by the interoperability mechanisms between the administration units. The e-services of the categories iii. and iv. should be enriched by additional mechanisms of historical information aggregation. This includes comparisons between the data profiles in different time periods. Such comparisons should be based on the dynamical models of the administration units. Mathematical Models of Data Integration Interoperability of public administration units, citizens and business should be the key concept in the future development of the e-government systems. The interoperability implies possibility of effective cooperation and information exchange between the public administration units. The necessary condition for the informational interoperability is data standardization and integration. Such standardization could be achieved through data representation in form of the feature vectors x j (t) and data matrix X(t) (1). Data integration should be based on adequate mathematical data models. It is widely accepted the fundamental role of the linear data models. We are taken in the consideration a general linear model in the following form Σ Σ a j (t k ) T x j (t k ) = c k=1,..., K j=1,..., m where a j (t k ) = [a j1 (t k ),...,a jn (t k )] T is the parameter (weight) vector of the j-th object O j (t k ) at the time moment t k. It could be seen, that by a specific choice of the parameter vectors a j (t k ), the basic discriminant or regression models can be specified [2]. The choice of the parameter vectors a j (t k ) means the imposing of some additional conditions on these vectors. It is done in order to avoid the overfitting phenomena and to obtain a model with a greater generalisation power. We are using the convex and piecewise linear (CPL) penalty and criterion functions for estimation of the parameter vectors a j (t k ) (e.q. [3]). The parameter vectors a j (t k ) * constituting minimum of the CPL criterion functions are using in definition of the optimal linear model (4). The basis exchange algorithms, similar to the linear programming allow to find efficiently minimum of the CPL criterion functions[4]. (4) Conluding Remarks The parameterisation of the administration units or enterprises O j (t k ) in the form of the feature vectors x j (t k ) (1) open the way for integration of public information. Key role is played here by choice of the standards of such parameterisation. For this purpose it is necessary to define the meaning and the measuring procedure of the each feature x i (t k ). The data representation in form of the matrices X(t k ) (1) allows to design mathematical models of public information integration.

5 Integration of Public Information at the Regional Level Challenges and Opportunities 533 References 1. O. R. Duda and P. E. Hart, D. G. Stork: Pattern Classification, J. Wiley, New York, R. A. Johnson, D. W. Wichern: Applied Multivariate Statistical Analysis, Prentice-Hall, Inc., Englewood Cliffs, New York, L. Bobrowski, M. Topczewska: "Tuning of diagnosis support rules through visualizing data transformations", pp in Medical Data Analysis, P. Perner et al., Springer- Verlag Berlin, Heidelberg L. Bobrowski: "Design of piecewise linear classifiers from formal neurons by some basis exchange technique". Pattern Recognition, 24(9), pp , 1991.