Artikel Epid The Association Between Blood Pressure and Incident Alzheimer Disease: A Systematic Review and Meta-analysis

Power, Melinda C.; Weuve, Jennifer; Gagne, Joshua J.; McQueen, Matthew B.; Viswanathan, Anand; Blacker, Deborah

Abstract

Background: Many epidemiologic studies have considered the association between blood pressure (BP) and Alzheimer disease, yet the relationship remains poorly understood.

Methods: In parallel with work on the AlzRisk online database ( www.alzrisk.org), we conducted a systematic review to identify all epidemiologic studies meeting prespecified criteria reporting on the association between hypertension, systolic BP, or diastolic BP and incident Alzheimer disease. When possible, we computed summary measures using random-effects models and explored potential heterogeneity related to age at BP assessment.

Results: Eighteen studies reporting on 19 populations met the eligibility criteria. We computed summary relative risks (RR[SIGMA]) for 3 measures of BP: hypertension (RR[SIGMA] = 0.97 [95% confidence interval = 0.80-1.16]); a 10-mm Hg increase in systolic BP (RR[SIGMA] = 0.95 [0.91-1.00]); and a 10-mm Hg increase in diastolic BP (RR[SIGMA] = 0.94 [0.85-1.04]). We were unable to compute summary estimates for the association between categories of systolic or diastolic BP and Alzheimer disease; however, there did not appear to be a consistent pattern across studies. After stratifying on age at BP assessment, we found a suggestion of an inverse association between late-life hypertension and Alzheimer disease and a suggestion of an adverse association between midlife diastolic hypertension and Alzheimer disease.

Conclusions: Based on existing epidemiologic research, we cannot determine whether there is a causal association between BP and Alzheimer disease. Selection bias and reverse causation may account for the suggested inverse association between late-life hypertension on Alzheimer disease, but, given the expected direction of these biases, they are less likely to account for the suggestion that midlife hypertension increases risk. We advocate continuing systematic review; the AlzRisk database entry on this topic ( www.alzrisk.org), which was completed in parallel with this work, will be updated as new studies are published.

artikel epid When Implausible Findings Emanate From High-quality Studies

Sorlie, Paul D.

Author Information

From the Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD.

Editors' note: Related articles appear on pages 532 and 544.

Correspondence: Paul D. Sorlie, NHLBI, NIH, 6701 Rockledge Dr, Bethesda, MD 20892. E-mail: sorliep@mail.nih.gov.

There are questions we as epidemiologists ask when we come across unanticipated findings. Is the result plausible? Does it fit with prior knowledge and expectations? Should I discard the result because it is so discordant with what we know that it suggests serious methodological error? We know the danger that we can fall into if we rely completely on prior expectations—medicine is replete with well-established evidence that is later refuted by new data. Discarding such findings can lead us to miss important new insights.

A case in point is the paper by Nguyen and colleagues¹ in this issue of Epidemiology, using data from the National Longitudinal Study of Adolescent Health (Add Health Study). The authors conducted a standard analysis to describe the mean blood pressure and percent of persons who were hypertensive in their sample population. They then compared the results with the national sample in the National Health and Nutrition Examination Survey (NHANES). Because their estimates were so discordant from the NHANES, they must have asked the plausibility questions described above. But, rather than give up, they proceeded to use this discordance as an opportunity to investigate reasons for the differences, with the hope of gaining some insight as to why measured blood pressure could differ so substantially between presumably similar populations.

Although blood pressure is a simple concept, it is extremely hard to measure accurately. Anyone who has worked with blood pressure measurement knows the long list of measurement difficulties. Measurement methodology could be the culprit for discordant findings. Possible reasons could be variations in cuff size, machine type, measurement setting, period of rest, technician training, or white-coat effect, just to name a few. Comparisons can also be influenced by differences in personal characteristics such as age, sex, race, social and economic status, obesity or body mass index, fitness, medication use, and population source and geography to name just some. Nguyen et al¹ investigated many of these possibilities. Nonetheless, in the end, they could not explain the differences. The results show that, for almost all of comparisons and adjustments, mean blood pressure is about 10 mm Hg higher in the Add Health Study than in NHANES for both systolic and diastolic pressures. This is a substantial discordance.

What then do we do when our analytic methods do not provide the anticipated answers? If we have measured without error all relevant variables, then we should (in principle) be able to explain all observed variations. The 2 key problems, however, are that we do not identify all the relevant variables and we certainly do not measure them without error. Consequently, we must keep looking for explanations.

There seem to be 2 missing pieces in the analysis. One is knowledge of the validity of the sphygmomanometer device itself as implemented in this field study, and the other is possible differences in population selection for this study compared with NHANES. Although the authors describe their testing of the pressure measurement in the automated device, they did not describe comparisons with a standard mercury device. The oscillometric sphygmomanometer used in the Add Health Study did undergo certification and approval by the British Hypertension Society, with none of the suggestions of bias as large as the magnitude of differences between the populations. However, the problem with oscillometric devices in general is that the actual algorithm to estimate the blood pressure values is proprietary. Consequently, as users, we never really know how blood pressure is estimated from the oscillometric measurements. As part of NHANES, the mercury device was compared with an oscillometric device (different from the one in this study). In NHANES, the oscillometric and mercury devices differed by a mean of less than 2 mm in the age group 20-49 years.² Thus, there is no clear reason why the sphygmomanometer used by Add Health Study personnel should have provided a higher average blood pressure. However, use of the device that estimates blood pressure from an algorithm, and with measurement by many different technicians under variable environments, might nonetheless result in unanticipated bias.

The issue of differences in results caused by differences in sampling is one for which we probably do not have the necessary information. The NHANES is a national sample of the US noninstitutionalized population, with a response rate to the examination of 75%. The Add Health Study began as a sample of students in grades 7 through 12, drawn from a representative sample of schools in the United States. In the initial recruitment, the response rate was 79%, and for Wave IV (source of data for this analysis) the response was 80% of those starting the study. The sample sizes are quite different, with the Add Health Study providing data for more than 14,000 people and NHANES more than 700—although for mean values of a measure such as blood pressure, 700 is sufficient to provide a very narrow confidence interval. The analyses by Nguyen et al¹ compare results by a variety of measured characteristics, but none can address the more undefined differences, such as the effect of a greater nonresponse in the Add Health Study or selection from schools as compared with household sampling.

Totally unexplained differences in well-designed and- implemented studies are not new. A classic case is the contradictory findings from 2 epidemiology studies (Framingham Heart Study³ and the Nurses' Health Study⁴) on the relation between postmenopausal estrogen use and coronary heart disease. In an editorial that accompanied the publication of both papers, after consideration of various reasons for the differences, Bailar stated that he “must resort to the investigator's great cop-out: More research is needed.”^5(p1081) For the estrogen question, there was subsequent research in the form of clinical trials that essentially resolved the question, and further research in observational populations has added additional insight.⁶ For the paper at hand, only a little more analysis can be done to prove the discrepancy. In the absence of explanations, I would stick with NHANES as the best estimate of mean blood pressure and hypertension in this age group because of its national sample design, “gold standard” sphygmomanometer, and long-term standardization and consistency. What to do with implausible findings? As was done in this study, discrepancies should be exposed to stimulate further inquiry.

ABOUT THE AUTHOR

PAUL SORLIE is Chief of the Epidemiology Branch at the National Heart, Lung, and Blood Institute, National Institutes of Health. He has been involved in the design and implementation of many of the large cohort studies funded by the National Heart, Lung, and Blood Institute and has had long-term research interests in the prevalence and determinants of elevated blood pressure.

REFERENCES

1. Nguyen QC, Tabor JW, Entzel PP, et al. Discordance in national estimates of hypertension among young adults. Epidemiology. 2011;22:532–541.

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2. Ostchega Y, Nwankwo T, Sorlie PD, Wolz M, Zipf G. Assessing the validity of the Omron HEM-907XL oscillometric blood pressure measurement device in a national survey environment. J Clin Hypertens. 2010;12:22–28.

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3. Wilson PW, Garrison RJ, Castelli WP. Postmenopausal estrogen use, cigarette smoking, and cardiovascular morbidity in women over 50: the Framingham Study. N Engl J Med. 1985;313:1038–1043.

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4. Stampfer MJ, Willett WC, Colditz GA, Rosner B, Speizer FE, Hennekens CH. A prospective study of postmenopausal estrogen therapy and coronary heart disease. N Engl J Med. 1985;313:1044–1049.

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5. Bailar JC III. When research results are in conflict. N Engl J Med. 1985;313:1080–1081.

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6. Hoover RN. The sound and the fury, was it all worth it? Epidemiology. 2008;19:780–782.

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artikel epid Work as Mental Health-promoting or Mental Health-depreciating?

Kelly, Inas Rashada,b; Dave, Dhaval M.b,c

Author Information

From the ^aDepartment of Economics, Queens College, City University of New York, Flushing, NY; ^bNational Bureau of Economic Research, Cambridge, MA; and ^cDepartment of Economics, Bentley University, Waltham, MA.

Correspondence: Inas Rashad Kelly, 300 Powdermaker Hall, 65–30 Kissena Boulevard, Flushing, NY 11367. E-mail: Inas.Kelly@qc.cuny.edu.

The pure investment form of Grossman's seminal demand-for-health model¹ proposes that people invest in health to raise productivity and earnings; thus there would be little point to investing in one's health postretirement, and the health stock is predicted to experience a sharp decline. Yet, as Grossman also points out, health has consumptive aspects. One has an incentive to invest in one's health even postretirement because, as economists would say, health directly enters the utility function. Put more simply, it makes one happy to be healthy. So perhaps retirees have a reason to care about their health after all, although the net effect on health may depend on which effect dominates.

In the absence of unambiguous theoretical predictions, the question of whether retirement has an adverse effect on mental and physical health remains an empirical one, and one that several studies have recently attempted to address. The question has important policy implications as we witness a trend toward earlier retirement in most developed nations, along with rising life expectancy and higher health care costs.

In this issue of Epidemiology, Oksanen and colleagues² carefully analyze the effect of retirement on mental health as measured by antidepressant use 4 years before and 4 years after retirement among a longitudinal cohort of Finnish employees. Finland had a low average retirement age of 59 years in 2004.³ However, the Finnish Pension Reform of 2005 made the retirement age more flexible and encouraged postponing retirement, with the increase in labor supply purported to lead to beneficial welfare effects.⁴ Oksanen et al suggest otherwise, at least with respect to mental health. Accounting for time-invariant factors such as socioeconomic status, sex, geographic area, and type of employer, and analyzing those who retired due to poor mental or physical health separately, the authors find some evidence that retirement may be beneficial for mental health. They also conclude that those who retired due to mental health issues showed postretirement improvements, in that they reported less antidepressant drug use. Diabetes medications, presumed not to be directly influenced by retirement, are used as a counterfactual. (No effects are found for diabetes drugs, as expected.)

The literature on this issue has been fragmented. In another study from Finland, Salokangas and Joukamaa⁵ also noted mental health improvements among retirees, but no clear effect on physical health. In contrast, Szinovacz and Davey⁶ found that depressive symptoms increases in women after retirement, especially if retirement is perceived as abrupt or forced. The effect is reinforced by the presence of a spouse with functional limitations.

Oksanen and colleagues² acknowledge that “the reasons for the[se] inconsistencies … may be due to differences in study design.” For one thing, the ways health is measured can lead to vast differences in results. Although the use of a more objective measure such as prescription drug use in the Oksanen study may bypass some misreporting issues, there are nonetheless concerns with this measure, particularly as a proxy for underlying mental health, for which reported measures (such as the DSM-III and CESD scales of depression) are heavily relied upon. Use of antidepressants, in addition to reflecting underlying mental health, also reflects health care access, physician contact, diagnosed mental illness, and decision to seek treatment, all of which may be affected by retirement due to shifts in resource, time, and income constraints.

Another key issue relates to endogeneity—in that health is not completely independent and is likely determined within the system being modeled. Endogeneity can arise from 2 sources. Structural endogeneity, or reverse causality, can occur because poor physical or mental health may be one reason to retire. This is reflected in the data used by Oksanen et al (notably in the Table 1 statistics and in eFigures 3 and 4) by a run-up in antidepressant use prior to retirement, especially among those who retire earlier than at the statutory age (suggesting poor health as an underlying factor). Moreover, statistical endogeneity implies that unobservable factors common to an individual may affect both his decision to retire and his health trajectory. Confounding factors abound, and they complicate the relationship. What are spouses up to, and how does their behavior affect the decision to retire? Does the person work in a field she is passionate about and enjoys, or is work unstimulating and stressful? Environmental factors related to the person's geographic area of residence may affect the outcomes as well, and whether she has hobbies and social activities outside of work. The possibility that standard of living could decline after retirement must also be considered (and varies considerably by socioeconomic status and country). Then, there is the simple fact that people age as they retire, and the rate of depreciation in one's health stock tends to increase at an accelerating rate over that age range. Oksanen and colleagues account for some of the time-invariant confounders, but not the time-varying ones.

With our colleague Jasmina Spasojevic,⁷ we have estimated the potential effects of retirement on various measures of physical and mental health, using longitudinal US data from the Health and Retirement Study spanning 1992 through 2005. Being able to observe health, other time-varying factors, and spousal behaviors over all waves, we accounted for the potential endogeneity of retirement and for confounding factors affecting the retirement decision. Our results consistently pointed to retirement having adverse effects on both physical and mental health on average. However, it is important to underscore the considerable heterogeneity that can be masked by average population effects. For instance, adverse effects on mental health tend to be larger on average among women than among men after retirement. Among those who are married and have social support, those who remain physically active after retirement, those for whom retirement was voluntary (excluding health as a reason), or those for whom retirement was partial, there were no adverse effects on mental health.

These results may help explain the divergent findings when countries with varying social programs are analyzed. They may also highlight the importance of identifying subgroups, and what may be most beneficial for them. Although retirement may be the optimal solution for those with mental health problems, it is unclear that retirement is beneficial for mental health or for physical health in general.

These studies and the results highlighted by Oksanen et al² have implications for social security programs in most industrialized nations. Gruber and Wise⁸ note that many countries structure their benefits so as to discourage work (eg, lowering lifetime benefits for people who work longer). With an aging population that is retiring earlier, and an unfunded liability facing public pensions, policy makers have pressed for several reforms including an increase in the retirement age. In the United States, social security data indicate that the retirement age for men has declined from 68.5 to 62.6 years and for women from 67.9 to 62.5 years; similar trends have been noted in Germany, Japan, and Sweden.^9,10 More recently, however, 63% of workers in the United States reported wanting to delay their retirement age in light of the poor economy and related factors.¹¹ Whether early retirement is individually or socially optimal depends on how retirement affects subsequent health status, among many other things. If retirement improves mental health, then this may lower health care spending among older adults. Evaluation of policies that seek to prolong retirement should account for this effect on health.

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ABOUT THE AUTHORS

INAS KELLY is an Assistant Professor of Economics at Queens College and Faculty Research Fellow at the National Bureau of Economic Research. Her research focuses on consumer choices and economic effects related to obesity, diabetes, nutrition, and exercise. DHAVAL DAVE is Associate Professor of Economics at Bentley University and Research Associate at the National Bureau of Economic Research. His research focuses on the analysis of health and labor policy and on the economics of health outcomes and behaviors, health insurance, and human capital.

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REFERENCES

1. Grossman M. The human capital model. In: Culyer AJ, Newhouse JP, eds. Handbook of Health Economics. Amsterdam: North-Holland; 2000.

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2. Oksanen T, Vahtera J, Westerlund H, et al. Is retirement beneficial for mental health? Longitudinal analysis of antidepressant use before and after retirement. Epidemiology. 2011;22:553–559.

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3. Finns retire on an earnings-related pension at about the age of 59 years. Finnish Centre for Pensions, statistical report 5/2006. Available at: http://www.etk.fi/Page.aspx?Section=41797&Item=26289.

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4. Lassila J, Valkonen T. The Finnish Pension Reform of 2005. Geneva Papers. 2007;32:75–94.

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5. Salokangas RK, Joukamaa M. Physical and mental health changes in retirement age. Psychother Psychosom. 1991;55:100–107.

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6. Szinovacz ME, Davey A. Retirement transitions and spouse disability: effects on depressive symptoms. J Gerontol B Psychol Sci Soc Sci. 2004;59:S333–S342.

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7. Dave D, Rashad I, Spasojevic J. The effects of retirement on physical and mental health outcomes. South Econ J. 2008;75:497–523.

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8. Gruber J, Wise D. Social security programs and retirement around the world: fiscal implications, introduction and summary. Cambridge, MA: NBER; 2005. NBER Working Paper Number 11290.

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9. Gendell M. Retirement age declines again in 1990s. Monthly Labor Rev. 2001;124:12–21.

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10. Gendell M. Trends in retirement age in four countries, 1965–95. Monthly Labor Rev. 1998;121:20–30.

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11. Helman R, Copeland C, VanDerhei J. The 2009 retirement confidence survey: economy drives confidence to record lows; many looking to work longer. Washington, DC: Employee Benefit Research Institute; 2009. Issue Brief 328.

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BASIS DATA

PENDAHULUAN

Sejarah basis data

sejak zaman dulu kala basis data sudah menjadi fokus yang utama pada Aplikasi. Pada awal tahun 1960, Charles Bachman di perusahaan General Electric mendesain generasi pertama DBMS yang disebut Penyimpanan Data Terintegrasi (Integrated Data Store). Dasar untuk model data jaringan terbentuk lalu distandarisasi oleh Conference on Data System Languages (CODASYL). Kemudian, Bachman menerima CM Turing Award (penghargaan semacam nobel pada ilmu komputer) tahun 1973.
Pada akhir tahun 1960-an, IBM mengembangkan sistem manajemen informasi (Information Management System). Hasil kerjasama antara IBM dengan perusahaan penerbangan Amerika mengembangkan sistem SABRE. Sistem SABRE memungkinkan user mengakses data yang sama pada jaringan komputer.
Pada tahun 1970, Edgar Codd di laboratorium penelitian di San Jose mengusulkan suatu representasi data baru yang disebut model data relasional.
Pada tahun 1980, model relasional menjadi paradigma DBMS paling dominan. Bahasa query SQL dikembangkan untuk basisdata relasional sebagai bagian proyek Sistem R dari IBM. SQL distandarisasi di akhir tahun 1980 dan SQL-92 diadopsi oleh American National Standards Institute (ANSI) dan International Standards Organization (ISO). Program yang digunakan untuk eksekusi bersamaan dalam basisdata disebut transaksi.
Pada tahun 1999, James Gray memenangkan Turing Award untuk kontribusinya pada manajemen transaksi dalam DBMS. Pada akhir tahun 1980 dan permulaan tahun 1990, banyak bidang sistem basisdata dikembangkan. Penelitian di bidang basisdata meliputi bahasa query yang powerful, model data lengkap, dan penekanan pada dukungan analisis data yang kompleks, sistem diperluas dengan kemampuan menyimpan tipe data baru misalnya image dan text serta kemampuan query yang kompleks.

PEMBAHASAN

 PENGERTIAN

Basis data atau database adalah kumpulan informasi yang disimpan di dalam komputer secara sistematik sehingga dapat diperiksa menggunakan suatu program komputer untuk memperoleh informasi dari basis data tersebut.

Konsep dasar dari basis data adalah kumpulan dari catatan-catatan, atau potongan dari pengetahuan. Sebuah basis data memiliki penjelasan terstruktur dari jenis fakta yang tersimpan di dalamnya. penjelasan ini disebut skema. Skema menggambarkan obyek yang diwakili suatu basis data, dan hubungan di antara obyek tersebut.

Ada banyak cara untuk mengorganisasi skema, atau memodelkan struktur basis data: ini dikenal sebagai model basis data atau model data. Model yang umum digunakan sekarang adalah model relasional, yang menurut istilah layman mewakili semua informasi dalam bentuk tabel-tabel yang saling berhubungan dimana setiap tabel terdiri dari baris dan kolom (definisi yang sebenarnya menggunakan terminologi matematika). Dalam model ini, hubungan antar tabel diwakili denga menggunakan nilai yang sama antar tabel. Model yang lain seperti model hierarkis dan model jaringan menggunakan cara yang lebih eksplisit untuk mewakili hubungan antar tabel.

Istilah “basis data” berawal dari ilmu komputer. Meskipun kemudian artinya semakin luas, memasukkan hal-hal di luar bidang elektronika, artikel ini mengenai basis data komputer. Catatan yang mirip dengan basis data sebenarnya sudah ada sebelum revolusi industri yaitu dalam bentuk buku besar, kuitansi dan kumpulan data yang berhubungan dengan bisnis.

Perangkat lunak yang digunakan untuk mengelola dan memanggil kueri (query) basis data disebut sistem manajemen basis data (database management system, DBMS).

 Alasan perlunya data base atau basis data :

• Basis data merupakan salah satu komponen penting dalam system informasi, karena merupakan dasar dalam menyediakan informasi.
• Basis data menentukan kualitas informasi : akurat, tepat pada waktunya dan relevan. Informasi dapat dikatakan bernilai bila manfaatnya lebih efektif dibandingkan dengan biaya mendapatkannya.
• Basis data mengurangi duplikasi data (data redudancy).
• Dengan mengaplikasikan basis data hubungan data dapat ditingkatkan.
• Basis data dapat mengurangi pemborosan tempat simpanan luar.

Bahasa basis data

Bahasa basis data terdiri atas :

Data Definition Language (DDL), merujuk pada kumpulan perintah yang dapat digunakan untuk mendefinisikan objek – objek basis data, seperti membuat sebuah tabel basis data atau indeks primer atau sekunder.

Data Manipulation Language (DML), mengacu pada kumpulan perintah yang dapat digunakan untuk melakukan manipulasi data, seperti penyimpanan data ke suatu tabel, kemudian mengubahnya dan menghapusnya atau hanya sekedar menampilkannya kembali.

Objektifitas Basis Data

pemanfaatan basis data dilakukan untuk memenuhi sejumlah tujuan (objektif) seperti berikut

a. Kecepatan dan kemudahan (Speed)

b. Efisiensi ruang penyimpanan (Space)

c. Keakuratan (Accuracy)

d. Ketersediaan (Avaibility)

e. Kelengkapan (Completeness)

f. Keamanan (Security)

g. Kebersamaan pemakaian (Sharability)

Perangkat lunak basis data

Perangkat lunak basis data yang banyak digunakan dalam pemrograman dan merupakan perangkat basis data aras tinggi (high level):

• Microsoft SQL server
• Oracle
• Sybase
• Interbase
• Xbase
• MySQL
• Microsoft Access

Dan masih banyak lagi perangkat lunak yang merupakan  aras tinggi (high level ) yang bisa di gunakan untuk basis data.

SQL (Structured Query Language)

SQL adalah bahasa yang digunakan untuk berkomunikasi dengan database. Menurut ANSI (American National Standards Institute), bahasa ini merupakan standar untuk relational database management systems (RDBMS):

Pernyataan-pernyataan SQL digunakan untuk melakukan beberapa tugas seperti : update data pada database atau menampilkan data dari database. Hampir semua software database mengimplementasikan bahasa SQL sebagai komponen utama dari produknya, salah satunya MySQL.

  

MySQL

Bahasa SQL memiliki struktur yang mudah dipahami karena perintah – perintahnya pada dasarnya dibuat dari bahasa Inggris. Sehingga kita dapat melakukan perintah – perintah SQL ke dalam database MySQL, yaitu

a. Memasukkan atau menambah record baru ke dalam database.

b. Mengeksekusi query database

c. Mengambil data dari database

d. Mengubah record pada database

e. Menghapus record pada database

Perintah SQL dapat diketik dengan huruf besar atau kecil (non case sensitive). Setelah selesai mengetik perintah di MySQL harus diakhiri dengan tanda titik koma sebagai penanda akhir dari perintah MySQL. Perintah SQL dapat diketik dengan huruf besar atau kecil (non case sensitive). Setelah selesai mengetik perintah di MySQL harus diakhiri dengan tanda titik koma sebagai penanda akhir dari perintah MySQL.

Selain perangkat lunak di atas , terdapat juga perangkat lunak pemrograman basis data aras rendah (low level), diantaranya :

• Btrieve
• Tsunami Record Manager
• Dan lain lain

 Menurut pengaksesannya, basis data dibedakan  menjadi 4 jenis yaitu :

1.      Basis data individual

Basis data individual adalah basis data yang digunakan oleh perseorangan. Biasanya basis data seperti ini banyak dijumpai dilingkungan PC. Visual dBASE, Corel Paradox, dan Filemaker Pro merupakan contoh perangkat lunak yang biasa digunakan untuk mengelola basis data untuk kepentingan pribadi.

2.      Basis data perusahaan

Basis data perusahaan adalah basis data yang dimaksudkan untuk diakses oleh sejumlah pegawai dalam sebuah perusahaan dalam sebuah lokasi. Basis data seperti ini disimpan dalam sebuah server dan para pemakai dapat mengakses dari masing masing computer yan bekedudukan sebagai client.

 3.      Basis data terdistribusi

Basis data terdistribusi adalah basis data yang disimpan pada sejumlah komputer yang terletak pada beberapa lokasi. Model seperti ini banyak digunakan bank yang memiliki sejumlah cabang di pelbagai kota dan melayani transaksi perbankan yang bersifat online.

4.      Basis data public

Basis data publik adalah basis data yang dapat diakses oleh siapa saja (publik). Sebagai contoh, banyak situs web (misalnya yahoo dan about.com) yang menyediakan data yang bersifat publik dan dapat diambil siapa saja secara gratis. Namun adakalanya seseorang harus menjadi anggota dan membayar iuran untuk memperoleh data publik.

 Perancangan basis data

Suatu data base dibangun berdasarkan kebutuhan informasi dalam suatu organisasi, oleh sebab itu pada umumnya perancangan data base dimulai dari pengamatan kebutuhan informasi. Berikut ini adalah langkah-langkah yang sering dilakukan dalam perancangan basisdata:

1. Teliti informasi apa yang dibutuhkan oleh organisasi ini, misalnya dengan me-wawancarai pengguna informasi dalam organisasi tersebut.
2. Pisahkan/kelompokkan  hasil temuan informasi menjadi beberapa entity.
3. Pikirkan field-data yang mendukung setiap entity
4. Tentukan field-data yang mungkin menjadi indeks (primary key) setiap entity
5. Pikirkan kemungkinan relasi antar entity
• bila one-to-one : berarti sebenarnya kedua entity ini bisa digabung
• bila one-to-many atau many-to-one : tambahkan primary-key dari entity sisi-one sebagai field-data baru pada entity sisi many.
• bila many-to-many : ciptakan sebuah file-relasi dengan field data utama adalah primary-key masing-masing entity yang berelasi, tambahkan field data yang baru apabila field data ini bergantung pada kedua primary key.
6. Pilih DBMS untuk melakukan implementasi, dimana setiap entity diciptakan sebagai sebagai sebuah table pada model relasional.

Contoh: Sistem Akademik pada umumnya membutuhkan informasi dasar sebagai berikut:

• Daftar Peserta Mata Kuliah (DPMK) : daftar per-mata kuliah yang memuat semua nama mahasiswa yang mengambil mata kuliah tersebut pada rencana studi-nya di awal semester.
• Daftar Nilai Akhir (DNA) : daftar per-mata kuliah yang memuat nama semua mahasiswa yang mengambil matakuliah tersebut disertai kode nilai yang akan dilingkari oleh dosen pengasuh di-akhir semester.
• Kartu Hasil Studi (KHS) atau Rapor: print-out untuk setiap mahasiswa dimana termuat hasil studi mahasiswa tersebut untuk setiap matakuliah yang di-ikuti-nya, disertai IPS (indeks prestasi semester)

Apabila ketiga informasi ini diteliti maka diperoleh domain data (entity) sebagai berikut:

1. Data Mahasiswa
2. Data Matakuliah
3. Data Dosen

Data Mahasiswa didukung oleh field-field data sebagai berikut:

• Nomer Mahasiswa
• Nama Mahasiswa
• Alamat
• Jenis Kelamin
• Agama
• Tgl Lahir
• dsb

Data Matakuliah didukung oleh field-field data sebagai berikut:

• Kode Matakuliah
• Nama Matakuliah
• SKS
• dsb

Data Dosen didukung oleh field-field data sebagai berikut:

• Kode Dosen
• Nama Dosen
• Alamat
• Keahlian
• dsb

Ketiga entity tersebut diatas memiliki primary-key masing-masing, yaitu: Nomer-Mahasiswa untuk entity Mahasiswa, Kode-Matakuliah untuk entity Matakuliah, dan Kode-Dosen untuk entity Dosen.

Langkah berikutnya adalah menentukan relasi antar entity tersebut:

Mahasiswa <–> MataKuliah : relasi ditandai dengan rencana studi, dimana satu mahasiswa dapat mem-program banyak matakuliah, dan sebaliknya satu matakuliah dapat diprogramkan oleh banyak mahasiswa, dengan kata lain relasi-nya many-to-many (M-to-N). Karena itu diperlukan file-relasi, yaitu file semester, dengan field-field data sbb:

• Kode matakuliah
• Nomer mahasiswa
• Nilai
• kode semester

Dosen <–> Matakuliah : relasi ini ditandai dengan penugasan dosen, misalnya di program S1, pada umumnya seorang dosen boleh mengajar lebih dari satu matakuliah, dan satu matakuliah hanya diajar oleh seorang dosen, dengan demikian relasi-nya one-to-many (1-to-M). Karena itu primary key dari dosen (kode-dosen) ditambahkan ke entity matakuliah. File data dosen nanti tidak ada perubahan, tetapi field dari file matakuliah akan bertambah, menjadi:

• Kode Matakuliah
• Nama Matakuliah
• SKS
• Kode-Dosen
• dsb

Kode-dosen pada file matakuliah disebut kunci-tamu atau foreign-key.

Dosen <–> Mahasiswa : relasi ini ditandai dengan fungsi dosen sebagai penasehat akademik (PA), dimana seorang dosen boleh menjadi PA lebih dari satu mahasiswa sementara setiap mahasiswa memerlukan satu PA, sehingga relasi yang cocok adalah one-to-many (1-to-M). Karena itu primary key dari dosen ditambahkan ke entity mahasiswa, sehingga susunan field-data mahasiswa menjadi sebagai berikut:

• Nomer Mahasiswa
• Nama Mahasiswa
• Alamat
• Jenis Kelamin
• Agama
• Tgl Lahir
• Kode-Dosen
• dsb

Pada akhirnya basisdata akademik ini paling tidak harus terdiri atas empat tabel/file yaitu: Tabel Mahasiswa, Tabel Mata kuliah, Tabel Dosen, dan Tabel Semester.

  

KESIMPULAN

Basis data atau database adalah kumpulan informasi yang disimpan di dalam komputer secara sistematik sehingga dapat diperiksa menggunakan suatu program komputer untuk memperoleh informasi dari basis data tersebut.

Basis data merupakan salah satu komponen penting dalam system informasi, karena merupakan dasar dalam menyediakan informasi.

Suatu data base dibangun berdasarkan kebutuhan informasi dalam suatu organisasi, oleh sebab itu pada umumnya perancangan data base dimulai dari pengamatan kebutuhan informasi.

 Sumber : mohon maaf  lupa