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Astronomical Data Analysis Software and Systems XIII ASP Conference Series, Vol. 314, 2004 F. Ochsenbein, M. Al len, and D. Egret, eds.

Mo deling and Developing the Information System for the Sup erAGILE Exp eriment
Francesco Lazzarotto DISP Tor Vergata & IASF-CNR, via del Fosso del Cavaliere, 100, 00133, Rome, Italy, e-mail: lazza@rm.iasf.cnr.it Enrico Costa, Ettore Del Monte & Marco Feroci IASF-CNR, Rome, Italy Abstract. We will present some formal description of the SuperAGILE (SA) detection system data, the relationships among them and the operations applied on data, with the aid of instruments such as EntityRelationship (E-R) and UML diagrams. We just realized functions of reception, pre-processing, archiving and analysis on SA data making use of Ob ject Oriented and SQL open source software instruments.

1.

Introduction

The data stream expected from the SuperAGILE instrument, onboard the AGILE gamma-ray mission, are continuous and massive flows (20 kb/s) of raw information sent to ground for a minimum of 3 years, plus a larger rate during ground tests (see figure 1). Data coming from the instrument concern physical measurements and equipment housekeepings. We have developed and are improving an information system to handle and archive the data produced at first by the prototypes and later on by the SA flight model. A big effort in the design phase has led us to achieve an integrated modular software system responding to most of the functions needed to extract knowledge from SA archives. 2. Requirements and Conceptual Design

The SuperAGILE experiment is described by processes, the processes scheme induces a scheme on the SA information system. We arrive at the goal of extracting scientific knowledge from SA data following these steps: first we pursued an accurate operation of requirements definition joined with reflections on our past work (Feroci et al. 1999; Lazzarotto 2001) and the study of some standards coming from other researches (see IVOA working team 2003). Then we developed an E-R scheme to have a conceptual level pro ject, modeling the data items and operations on them. It describes concepts of interest to the application s such as detectors, analogue and electronical hardware, software components and operators and the relationships among these concepts, in a simple but precise way, not computer specific (see Fig. 2). At this level we isolated some metadata 464 c Copyright 2004 Astronomical Society of the Pacific. All rights reserved.


The Information System for the SuperAGILE Experiment

465

Figure 1. cations

Use cases for SA ground and flight prototypes data appli-

describing measurement data. The metadata will be contained in the persistent Data Base Management System (DBMS), massive measurement data are stored in a repository (e.g. large computer hard-disks).

3.

Performance Study and Logical Design

Logical rendering of the conceptual model materializes in the realization of a relational database scheme (Fig. 3) for 'run' (measurement or simulation) metadata. Also the scheme of scientific contents data is realized following the ob ject oriented and the relational paradigms. The definition of a metaformat for experimental data and a formal model for the operations on them, make it possible to do a performance study on the applications (see part of the model in the Operation Table (Table 1)).

Table 1.

SA data operation table Operation Type(I/B) ReadData I CreateAddLUT B CreateResMat B AddCalibrate I EnCalibrate I GetDetImg I GetSpectrum I GetLightCurve I GetSkyImage I

Frequency 50 per day 1 per month 1 per day 50 per day 50 per day 20 per day 20 per day 20 per day 15 per day


466

Lazzarotto, Del Monte, Feroci & Costa
SA resource

is a dc

SA Data

SA software

SA document

is a

dc

Diagnostic

Simulation

Measurement

SA data
composing

composing
HouseKeeping Science Calibration Log Time Sync Conf Equipment specific

composing

RegIO

SA metadata

SA input data

SA output data
Environmental Ratemeters Engineeristic

Figure 2. 4.

Composition and gerarchy of SA data

Physical Scheme of DBMS and Architecture of the Analysis Software

To handle the experiment archive we use a DBMS based on SQL to store metadata realizing a RAM persistent short pro jection of the repository. We save archiving, summarizing instrumental information and textual information from the acquisition logfile. The content of the database image at a certain time is given in html format and put in a public web area, from where every authorized user can read useful information and download desired files with ftp links. The procedures are written in C++ embedding mySQL and science libraries, Root (Brun 1997; see also http://root.cern.ch), cfitsio (Pence 1999; see also http://heasarc.gsfc.nasa.gov/docs/software/fitsio/fitsio.html ), AstroRoot (Beck 2004; see also http://isdc.unige.ch/index.cgi?Soft+astroroot ). The implementation is also realized with the help of a C/C++ interpreter "Cint" that permits development and easy tests of C++ code macros. We also used postgresSQL to implement DBMS functions, the linking with the Root library is more efficient with mySQL. For data analysis software we use OO approach, (a simple classes scheme is reported in figure 3), it implements functions analysis operations and produces higher level of abstraction scientific data products such as light curves, detector images and spectra. 5. The Final Goal of the System: Scientific Knowledge Extraction Applications structured reports regarding automatic analysis on large other metadata saved in the DBMS archive. Then it will mparative studies on SA data sets, with the application of and Computer Science techniques. The results of analysis

The idea is to save amounts of data, in be possible apply co advanced statistical


The Information System for the SuperAGILE Experiment
SADAS Eventlist SAAddressLUT SAResMat ReadData():Eventlist CreateAddLUT():SAAddrLUT CreateResMat():SAResMat AddCalibrate(SAAddrLUT, Eventlist):Eventlist EnCalibrate(SAResMat, Eventlist):Eventlist

467

Eventlist Event GetDetImg():SADetectorImage GetSpectrum():SASpectrum GetLightCurve():SALightCurve
(1,N) detection system configuration

source (1,N)

detection

is a is a

Campaign

measurement

operator

CaliEventList

ScieEventlist

CaliEvent

ScieEvent

transmission (1,N)

storage

SA data

SAAddrLUT

SAResMat

SALightCurve show()

SASpectrum show()

SADetectorImage show() GetSkyImage():SASkyImage

(1, N)

SASkyImage
archive saving science console

Figure 3. SA measurement session conceptual scheme and classes for sadas software operators will be stored in compact data sets rendered, for istance, in XML. We created a raw prototype of this system in 2001 using SAX mission data archives, then we defined the whole Scientific Knowledge Extraction (SKE) process for our contest expressed above, suggesting the approach we are developing and testing now on laboratory and simulations SA data with the purpose of applying the refined and tested SKE system also to SA flight data soon after its launch in 2005. References Beck, M. et al. 2004, this volume, 436 Brun, R. & Rademakers, F. 1997 ROOT - An Ob ject Oriented Data Analysis Framework, Proceedings AIHENP'96 Workshop, Lausanne, Sep. 1996, Nucl. Inst. & Meth. in Phys. Res. A 389, 81-86 Derriere S. et al 2004, this volume, 315 Derriere, S., Ochsenbein, F., Boch, T., & Rixon, G. T. 2003, in ASP Conf. Ser., Vol. 295, ADASS XII, ed. H. E. Payne, R. I. Jedrzejewski, & R. N. Hook (San Francisco: ASP), 69 Feroci, M. et al, 1999, astro-ph/9912488, Proceedings of the 5th Huntsville Gamma-Ray Bursts Symposium, 711-715 IVOA working team 2003, Resource Metadata for the VO (http://www.ivoa.net) Lazzarotto, F., 2001, Computer Science Thesis at "La Sapienza", Rome Pence, W. D. 1999. in ASP Conf. Ser., Vol. 172, ADASS VIII, ed. D. M. Mehringer, R. L. Plante, & D. A. Roberts (San Francisco: ASP), 487 Wenger, M. et al. 2000, A&AS, 143, 9